Real-Time Integration of Internal Auditing into AIS Frameworks for Monitoring Bank Balances

Real-Time Integration of Internal Auditing into AIS Frameworks for Monitoring Bank Balances | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Real-Time Integration of Internal Auditing into AIS Frameworks for Monitoring Bank Balances Saeed Askari Nia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9709682/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the integration of internal audit functions into Accounting Information Systems frameworks to enable continuous, real-time monitoring of bank balances. Leveraging technologies such as artificial intelligence, big data analytics, and cloud computing, this integration aims to facilitate early anomaly detection, fraud risk reduction, and enhanced regulatory compliance. A convergent mixed-methods approach was employed: questionnaire data (n = 260) from Iranian banking professionals were analyzed using non-parametric tests (Spearman's correlation) and hierarchical regression, complemented by thematic analysis of semi-structured interviews with 15 experts. Quantitative findings provide partial support for the main hypothesis, confirming a positive relationship between AIS-internal audit integration and financial reporting accuracy, as well as a significant moderating role of organizational process integration. However, no significant effects were found for anomaly detection speed, data transparency, fraud risk reduction, or the moderating role of auditors' skills (despite a significant overall model in some regressions). Qualitative insights reveal substantial potential benefits (e.g., real-time analysis, inter-unit synergy) alongside key barriers (e.g., infrastructure weaknesses, skills gaps, security concerns). Triangulation highlights that observed quantitative non-significances may stem from contextual limitations in emerging markets, while qualitative data suggest these benefits could materialize with targeted improvements (e.g., training, standards development). The study advances theoretical understanding of continuous auditing in digital banking and offers practical implications for risk management and AI governance in resource-constrained settings. Finance Internal Audit Accounting Information Systems Real-time Monitoring Bank Balance Continuous Auditing 1. Introduction The rapid digitization of financial services has rendered strong, real-time oversight and proactive risk management indispensable for modern banking institutions. Accounting Information Systems (AIS) serve as the infrastructural backbone, playing a pivotal role in processing, recording, and reporting financial transactions, thereby enhancing operational efficiency and regulatory compliance [ 4 ][ 5 ]. The seamless integration of internal audit functions into AIS architectures represents a strategic advancement, particularly in an environment where the banking sector faces an escalating need for instantaneous monitoring of account balances to prevent fraud, optimize liquidity, and align with a dynamic and evolving regulatory landscape [ 1 ]. Despite remarkable technological progress, persistent challenges such as massive data volumes, limitations of legacy systems, and continued reliance on periodic audits have impeded the realization of real-time assurance. Existing literature has largely examined AIS and internal auditing in isolation, with limited attention to their synergies in the context of continuous monitoring of bank balances [ 9 ][ 13 ]. The present study addresses these gaps through an innovative mixed-methods approach, emphasizing the transformative role of artificial intelligence while prominently considering its governance implications, including algorithmic bias, transparency, and ethical concerns. This convergent mixed-methods design, which interweaves robust statistical analyses with deep qualitative insights, remains relatively rare in the digital banking domain and contributes to the development of effective corporate governance frameworks for the application of AI in continuous auditing (CA) [ 3 ][ 14 ]. 1.2 Importance of the research topic Banks operate in a highly competitive environment characterized by stringent regulations, where high transparency, rapid decision-making, and continuous regulatory compliance are critical imperatives. Internal auditing, which was traditionally viewed primarily as an assurance-oriented and backward-looking activity, has evolved into a dynamic and well-established function within corporate governance. This function plays a pivotal role in ensuring the effectiveness of internal controls and the reliability of real-time data generated by modern Accounting Information Systems (AIS) [ 4 ]. Real-time monitoring of bank balances enabled by technological advancements such as open banking APIs, automated transaction reconciliation, and high-speed data processing has now become essential and indispensable for effective liquidity management, early fraud detection, and accurate, timely reporting to regulatory authorities [ 5 ]. Prior research indicates that AIS significantly enhances the efficiency and quality of internal audit processes by automating routine data collection and processing tasks, improving the timeliness and accuracy of audit reporting, and strengthening risk assessment capabilities. More recent studies emphasize that the synergistic integration of AIS with internal audit functions not only assists banking institutions in meeting regulatory requirements but also contributes to optimizing overall organizational performance and reinforcing corporate governance structures [ 1 ][ 6 ]. For example: Focusing on the importance of integrating internal audit in the realization of the added value of information systems, Alassuli has confirmed the positive moderating effect of this function on the relationship between AIS implementation and the performance of banks. Research such as Steavan et al. and Alzban have shown the prominent role of AIS in preventing fraud and reducing operational errors, especially when these systems are complemented and synergized with robust internal audit mechanisms. Ahmed et al. (2021) and Wen et al. (2019) have pointed to the increasing use of AI tools in AIS and emphasized the transformative potential of these technologies in achieving automated financial assurance and real-time monitoring. Despite significant advances in technology, several challenges remain in the way of integrating AIS with internal audit functions for real-time monitoring: Technical and infrastructural complexities: Large volumes of data (big data), limitations of legacy systems, and integration issues that make it difficult to integrate audit tools seamlessly and often lead to the generation of false positives or inability to process in real-time. Continuity of traditional approaches: Many banks continue to rely on periodic and post-incident audits, which are incompatible with the high speed of transactions and the dynamic risks of digital banking, and miss opportunities for early detection. Limited focus of previous research: The available literature has often examined AIS and internal audit separately, and few studies have addressed their synergies in real-time frameworks, particularly bank balance monitoring, liquidity management, and regulatory compliance. Gaps in standards and practices: Lack of standard frameworks for auditing AI-based systems, practical implementation mechanisms, best practices for ongoing assurance, as well as ethical issues such as algorithmic bias, transparency, and data privacy. These challenges and gaps, despite the high potential of new technologies, have seriously hindered the full realization of continuous and preventive monitoring and require further empirical research and the development of governance standards. 1.3. Research logic Considering these gaps and high risks caused by inadequate supervision such as fraud, regulatory fines, and damage to organizational reputation, the present study was conducted with the aim of systematically investigating how to optimally integrate internal audit into AIS architectures for real-time monitoring of bank balances. This study seeks to answer the following key questions [ 3 ]: How does the integration of internal audit into AIS promote real-time monitoring of bank balances? What role do moderating factors (such as auditors' expertise and process integrity) as well as organizational, technological, and supervisory factors influence this merger? What implications does this merger have for the dominance of AI in banking auditing? To answer these questions, the main hypothesis of the research: the integration of internal audit into accounting information systems (AIS) frameworks increases the effectiveness and accuracy of real-time monitoring of bank balances. The sub-hypotheses include: The integration of internal audit into the AIS has a significant positive relationship with the accuracy of real-time bank balance reports. Integrating internal audit into the AIS increases the speed of detection of banking errors and anomalies in real time. Integrating internal audit into the AIS increases the transparency and reliability of real-time banking data. The skills and expertise of internal auditors play a positive moderating role in the effect of merger on real-time monitoring. Integrating internal audit into AIS reduces the risks of fraud and misuse of bank balances in real time. The degree of integration of the organization's financial and control processes moderates the intensity of the impact of the merger on real-time monitoring. By bridging the gaps between audit theory and the procedures of modern information systems, this research will contribute to a comprehensive understanding of the following aspects: The effectiveness and challenges of integrating internal audit with AIS for real-time monitoring. The role of automation, artificial intelligence, and open banking technologies in facilitating real-time internal audit activities. Best practices to achieve reliable and consistent assurance in digital banking environments. This research has been conducted at a critical and pivotal juncture, holding substantial importance for both scientific and practical advancements. It offers valuable insights to banking professionals, internal auditors, information systems designers, and regulatory authorities. The ultimate goal is to bridge the identified gaps in the literature through an integrated and interdisciplinary approach to real-time banking risk management and control an approach that appears increasingly indispensable in an era characterized by instantaneous transaction flows and intricate regulatory demands. The present study employs a convergent mixed-methods design, the primary innovation of which lies in leveraging triangulation of quantitative and qualitative findings to achieve a more comprehensive and multidimensional understanding of the phenomenon under investigation [ 3 ]. Quantitative component: Data were collected using a structured questionnaire designed based on the key research dimensions (AIS integration, real-time monitoring, data accuracy and quality, system integration, information security, and internal audit effectiveness) from professionals employed in the banking sector. The Kolmogorov-Smirnov test results indicated non-normality of the data distribution; consequently, non-parametric tests were employed: Spearman correlation to examine relationships among variables and hierarchical regression to evaluate moderating effects. Qualitative component: Semi-structured interviews were conducted with key experts, and the resulting data were analyzed using Braun and Clarke’s (2006) thematic analysis method. In this process, over 830 meaningful statements were extracted and grouped into primary and secondary themes. Research hypotheses: These include one main hypothesis (integration of internal audit into AIS enhances the effectiveness and accuracy of real-time monitoring of bank balances) and six subsidiary hypotheses (relationship with reporting accuracy, increased speed of anomaly detection, improved transparency, moderating role of auditor expertise, reduced fraud risk, and moderating role of organizational process integration). Methodological innovation: The adopted mixed-methods approach enabled deeper explanation and interpretation of quantitative results particularly the rejected hypotheses through qualitative insights, leading to the identification of significant practical implications, especially in the domain of AI governance (such as ethical challenges, algorithmic bias, and the need for transparency). This triangulation not only strengthened the internal validity of the study but also facilitated the development of more practical and realistic recommendations. The theoretical framework of this research is grounded in the principles of agency theory and the corporate governance literature. According to agency theory, information asymmetry between managers (agents) and stakeholders (principals) creates opportunities for opportunistic behavior and heightens the risk of financial fraud. The integration of internal audit functions into AIS frameworks by providing real-time data, enhancing transaction transparency, and strengthening traceability can mitigate this asymmetry and significantly reduce agency costs [ 4 ]. From a corporate governance perspective, internal auditing constitutes a core pillar of the internal control system and a key mechanism for supporting the audit committee and board of directors in monitoring financial and operational risks. In the digital banking environment, where transaction volume and velocity have increased dramatically, reliance solely on traditional periodic audits is insufficient. Transitioning toward CA based on AIS and emerging technologies such as artificial intelligence and big data analytics represents a strategic imperative [ 18 ]. Drawing further inspiration from contemporary approaches to data governance and AI governance, this research views the integration of internal auditing and AIS not merely as a technical tool but as a governance mechanism for ensuring transparency, accountability, and organizational-level risk management. When supported by adequate infrastructure and sufficient specialized skills, this mechanism can substantially enhance the quality of financial reporting and the effectiveness of monitoring bank balances. 2. Review of the literature Based on a comprehensive literature review, the integration of internal auditing within AIS frameworks for real-time monitoring of bank balances represents a dynamic and rapidly expanding field, primarily driven by technological innovations. The central role of this integration lies in transforming internal auditing from a traditional, periodic, and reactive function into a continuous, proactive, and predictive process [ 8 ]. Key findings from the literature indicate that advanced technologies such as AI, big data analytics, and cloud computing are the primary drivers of this transformation. By enabling real-time processing and analysis of massive datasets, continuous transaction monitoring, and early detection of anomalies and potential fraud, these technologies significantly enhance the accuracy, efficiency, and overall effectiveness of internal audit processes in the banking sector. Established frameworks such as COBIT and COSO provide robust foundations for IT governance and internal controls. Meanwhile, emerging conceptual models for predictive auditing and AI assurance frameworks are taking shape to guide the secure and effective implementation of these advanced technologies [ 5 ]. Research methodologies in this domain are increasingly quantitative, employing sophisticated techniques such as partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms to validate and evaluate the effectiveness of integrated audit and AIS systems. These quantitative approaches enable the testing of complex inter-variable relationships and the prediction of performance in real-world banking environments. Nevertheless, the literature simultaneously highlights several notable gaps. One of the most significant is the scarcity of empirical and focused studies that specifically address the intricacies of real-time monitoring of bank balances as a distinct and independent function. Furthermore, fundamental challenges persist, including the absence of accepted standards for auditing AI-based systems, risks of algorithmic bias, serious concerns regarding data quality and integrity, cybersecurity and privacy issues, and the need to develop new specialized competencies for internal auditors. Although there is broad consensus on the potential benefits of this integration, fully realizing its potential requires coordinated, multifaceted efforts to address these practical, technical, and ethical challenges. The present study, with its emphasis on empirical aspects and adoption of a mixed-methods approach, takes a step toward bridging these gaps [ 6 ]. 2.1 The Evolving Role of Internal Audit in Real-Time Financial Supervision The integration of internal audit functions into AIS frameworks represents a fundamental paradigmatic shift in financial oversight, particularly in response to the banking sector's critical need for real-time monitoring of account balances. This evolution is primarily driven by the increasing complexity of financial transactions, heightened regulatory requirements, and the transformative power of emerging technologies [ 4 ]. Modern internal auditing has transcended its traditional role characterized by periodic reviews and a focus on regulatory compliance to become a strategic, continuous, and fully technology-driven process. The primary objective of this process is to deliver real-time assurance, proactive risk management, and advanced fraud detection [ 5 ]. The synergy between a robust AIS and advanced internal audit capabilities is essential for ensuring data integrity, information security, the overall reliability of financial reporting in banking institutions. This integration not only enhances operational efficiency but also provides a solid foundation for digital corporate governance in the era of modern banking [ 7 ]. 2.2 The Fundamental Impact of AIS and Internal Audit on Banking Performance A well-designed and effectively implemented AIS constitutes the primary foundation for efficient management and accurate financial reporting in any banking institution. Numerous studies have consistently confirmed that AIS exerts a positive and substantial impact on the accuracy of financial reporting through process automation, reduction of human errors, assurance of data integrity and consistency, creation of transparent audit trails, and enabling timely reporting [ 12 ]. For instance, a study on commercial banks in Jordan demonstrated that successful AIS implementation is associated with significant improvements in banking performance indicators, particularly when internal auditing acts as a moderating factor that amplifies the positive effect of AIS on overall bank performance. These findings underscore the importance of synergistic integration between AIS and internal audit mechanisms, illustrating that the true value of such systems is maximized only when complemented by robust and effective internal oversight and controls [ 7 ]. A key insight from this research is its identification of internal auditing as a strong moderating variable that significantly enhances the positive impact of AIS implementation on overall bank performance. This finding highlights the strategic imperative for banks not only to adopt advanced AIS but also to deeply and cohesively integrate them with rigorous internal audit functions, thereby fully realizing operational, managerial, and strategic benefits. Although a Computerized Accounting Information System (CAIS) provides the essential infrastructure for recording and processing financial data, its effectiveness in fraud detection is often limited particularly when used in isolation without advanced analytical support. A 2023 study revealed that while big data analytics and internal auditing exert positive and significant effects on accounting fraud detection, a computerized accounting information system alone does not demonstrate a meaningful impact in this regard (Proceedings of the 8th International Conference on Big Data and Computing, 2023). This finding emphasizes that the true value of an AIS becomes evident only when augmented by advanced analytical capabilities (such as machine learning algorithms) and precise internal audit oversight. In such cases, the system evolves from a mere recording tool into a dynamic platform for internal control, risk analysis, and proactive fraud detection. 2.3 Strategic shift to integrated, risk-based auditing The field of internal auditing has undergone remarkable evolution, transforming from a basic, primarily operational financial oversight function into a strategic pillar of corporate governance one that encompasses not only financial monitoring but also the management of organizational culture, information technology challenges, and comprehensive organizational risks. This evolution has been particularly accelerated by global financial crises and the growing demands from stakeholders for greater transparency and heightened accountability. Two fundamental philosophical shifts characterize this new era of internal auditing: Moving towards integrated auditing, in which internal controls and financial statements are reviewed simultaneously and synergistically; and the widespread adoption of the risk-based internal auditing (RBIA) approach, which focuses audit resources on high-risk areas and creates more added value for the organization. These changes have transformed internal audit from a reactive and compliance-oriented role to a strategic partner in board and senior management decisions. 2.4 Integrative Audit Excellence Professional organizations and standard-setters are increasingly endorsing the integrated auditing approach, a method in which the audit of internal controls over financial reporting (ICFR) is performed concurrently and synergistically with the audit of financial statements (FS). This approach stands in contrast to separated auditing, where these two activities are conducted independently and without coordination. Empirical evidence from a study on publicly listed Chinese companies indicates that integrated auditing offers significant advantages, leading to improved financial reporting quality and enhanced audit process efficiency. The primary driver of this superiority is identified as the knowledge spillover effect, whereby insights and deep understanding gained from auditing internal controls directly enhance the effectiveness and efficiency of financial statement audits, and vice versa. This synergistic effect not only eliminates redundant and unnecessary procedures but also provides a more comprehensive and integrated view of the organization's control environment and overall financial health, ultimately contributing to strengthened corporate governance and greater assurance for stakeholders [ 7 ]. 2.5 Centrality of Risk-Based Internal Audit (RBIA) One of the foundational pillars of modern internal auditing in the banking sector is the RBIA approach. This approach shifts the focus from routine, compliance-oriented testing to the strategic and targeted allocation of audit resources based on a comprehensive and dynamic assessment of organizational risks. By prioritizing high-risk areas, RBIA ensures that the most critical domains receive the greatest attention and resources, thereby significantly enhancing the value-added and overall efficiency of internal audit performance. Successful implementation of this approach is heavily influenced by key organizational factors, including sustained commitment from senior management to a risk management culture, the quality and comprehensiveness of risk management training programs for internal auditors, and the level of comprehensive leadership support for risk-based audit processes. Collectively, these factors facilitate the institutionalization of RBIA and transform it into an effective tool for strengthening corporate governance and proactive risk management in banks. From a theoretical perspective, the RBIA approach is rooted in Agency Theory, which emphasizes the existence of information asymmetry between managers (agents) and shareholders (principals). The theory argues that robust oversight mechanisms such as effective internal auditing are essential for aligning managers' interests with those of shareholders, reducing opportunistic behaviors, and ultimately lowering agency costs. Integrating a realistic real-time monitoring system with a comprehensive, risk-based internal audit approach empowers banks to substantially reduce the risks of deception and financial misrepresentation, bolster stakeholder confidence, elevate overall organizational performance. This synergy not only supports proactive risk management but also positions internal auditing as a strategic partner in creating sustainable value and reinforcing corporate governance. 2.6 Technology Transformation: Factors Affecting Real-Time Monitoring The ability to perform real-time monitoring of bank balances is almost entirely dependent on the integration of advanced technologies into AIS and internal audit frameworks. These technologies go beyond mere tools, playing a pivotal role in redefining the capabilities of internal auditing. Collectively, they facilitate a fundamental shift from reactive auditing which identifies issues after they occur to proactive and predictive auditing, which anticipates and mitigates risks in real time. Big Data Analytics (BDA): The high volume of transactions in modern banking gives rise to the phenomenon known as Big Data. BDA enables auditors to perform real-time analysis of vast and diverse datasets, examining the entire data population rather than relying on traditional sampling methods. The application of BDA in bank internal audits particularly when integrated with Internet of Things (IoT)-based “continuous auditing (CA)” systems makes instantaneous monitoring of financial and operational activities possible. This data-driven approach facilitates early identification of concealed fraud and compliance issues, substantially reducing the risk of financial misrepresentations [ 9 ]. Artificial Intelligence (AI): AI, encompassing machine learning (ML), natural language processing (NLP), and predictive analytics, is fundamentally transforming audit processes. AI-based tools can automate repetitive and time-consuming audit tasks, detect anomalies and complex patterns that may indicate fraud, and dramatically improve the overall accuracy and speed of the audit process. In the digital banking domain, AI-enhanced cloud infrastructure provides unparalleled capabilities for real-time data analysis, anomaly detection, and predictive risk modeling. Specifically, real-time fraud detection systems powered by AI designed tailored for the financial services industry significantly increase fraud identification accuracy while minimizing false positive rates [ 10 ][ 11 ]. Cloud Computing : The adoption of cloud technology has introduced a new, more flexible model for financial auditing. Cloud platforms facilitate seamless data sharing and transfer, enabling remote and real-time monitoring of financial audit information without the need for extensive on-premises infrastructure [ 5 ]. Blockchain Technology : Although blockchain remains an emerging technology in this field, it holds considerable potential for enhancing security, transparency, and integrity in banking transactions. Its integration with AI can create a powerful system for real-time transaction monitoring, where the immutable nature of the blockchain ledger ensures data integrity,AI algorithms identify and flag fraudulent activities as they occur. 2.6 Key Theories, Frameworks, and Methods in Modern Auditing The integration of internal auditing into AIS for real-time monitoring is guided by a combination of established theories, corporate governance frameworks, and innovative methodologies. Underlying Theories Agency : As previously discussed, this theory provides the rationale for oversight mechanisms such as internal auditing to monitor management actions and reduce information asymmetry between managers (agents) and stakeholders (principals). Information Economics : This theory frames the value of information, emphasizing how accurate, timely, and relevant data from an AIS can improve decision-making and market efficiency objectives central to real-time monitoring. Structuration : This theory can be applied to understand the dynamic interaction between human auditors (agency) and technological/organizational structures (e.g., AIS and regulations), which mutually shape and are shaped by auditors' actions. Corporate Governance and Implementation Frameworks A variety of frameworks are employed to oversee, implement, and evaluate these integrated systems. The table below summarizes some of the key frameworks identified in the literature. These frameworks offer a structured approach to managing the complexities of modern IT environments, ensuring that the integration of internal auditing and AIS is both effective and compliant with established standards. Table 1 Key Frameworks in the Literature Framework Description Key Focus Relevant Studies COBIT A comprehensive framework for the governance and management of enterprise IT. Provides control objectives to reduce audit risks and ensure the reliability and security of electronic AIS. Ghadeer (2022) 15 COSO A framework for internal control, risk management, and fraud deterrence. Offers guidance on establishing effective internal controls, which is a foundational element for internal audit effectiveness. Olawale et al. (2022) 20 Predictive Audit Frameworks Conceptual models that integrate AI/ML for real-time financial risk management. Enables a shift from reactive to proactive auditing by using predictive analytics for anomaly detection and continuous monitoring. Olorunyomi et al. (2022) 13 Quality Assurance for AI Protocols for managing the quality and integrity of AI algorithms used in finance. Focuses on bias detection, regulatory compliance, real-time performance monitoring, and continuous model validation. Kuna (2025) 19 Data Quality by Design A methodology (based on ISO/IEC 25012) for embedding data quality into IS development. Ensures foundational data integrity and reliability, which is critical for the accuracy of any real-time monitoring system. Guerra-García et al. (2023) 21 2.7 Research methods in literature Researchers exploring this domain employ a diverse array of research methodologies, each addressing different dimensions of the phenomenon of integrating internal auditing with AIS. Quantitative methods, particularly Partial Least Squares Structural Equation Modeling (PLS-SEM), widely used to test complex relationships among variables such as AIS implementation quality, internal audit efficiency, organizational performance. This method enjoys high popularity in banking research due to its flexibility in handling smaller samples and non-normal data distributions [ 9 ]. In addition, bibliometric analysis is utilized to map the historical evolution of research, identify primary themes, and highlight technological transformations such as the shift from traditional auditing to AI-based auditing in the context of internal audit effectiveness. This approach enables the examination of citation networks, co-authorship patterns, and keywords, providing a comprehensive picture of scientific advancements in the field [ 10 ]. Many studies have also focused on developing conceptual frameworks to propose new models for incorporating advanced data analytics and artificial intelligence technologies into audit processes. These models often serve as a foundation for future empirical research and contribute to a deeper understanding of the operational and governance mechanisms underlying this integration. The diversity of these methods from advanced quantitative analyses to qualitative approaches and bibliometrics reflects the growing maturity of the field and underscores the necessity of adopting multidisciplinary perspectives to address the complexities of digital banking [ 11 ]. 2.8 Gaps and limitations identified in the literature Despite the rapid advancements and growing volume of research, the literature reveals several notable gaps and limitations that warrant further investigation: Lack of specific focus on real-time bank balance monitoring : A primary gap is the scarcity of studies that specifically examine the integration of internal auditing into AIS for real-time monitoring of bank balances as a distinct function. While many articles discuss real-time capabilities in the broader contexts of fraud detection or CA, the specific nuances, challenges, and audit procedures related to bank balance monitoring (e.g., ensuring liquidity, overseeing large transactions, and real-time reconciliation) remain largely underexplored. Absence of standardized AI auditing methods : The adoption of artificial intelligence in auditing has outpaced the development of frameworks and standardized methods for auditing AI systems themselves. There is an urgent need for consistent and verifiable standards for auditing AI bias, algorithmic transparency, and model validation to ensure the reliability and consistency of these systems. Need for more empirical evidence : Although numerous conceptual papers address the benefits of AI and BDA, there is a relative dearth of empirical research on the long-term effectiveness, feasibility, broader organizational impacts of these technologies in real-world banking environments. Additional longitudinal studies are required to move beyond theoretical advantages to practical, evidence-based outcomes. Implementation challenges : Practical barriers to implementation are frequently acknowledged but rarely examined in depth. These include managing data privacy and security in real-time systems, overcoming significant skills gaps among auditors who require training in data analytics and AI, and navigating the high costs and complexities of integrating new technologies into legacy AIS frameworks. The human element : As automation increases, the evolving role of the human internal auditor requires further study. Research is needed on how auditors interact with AI-based systems, interpret (or override) data outputs, and address emerging ethical dilemmas arising from reduced human oversight. 2.9 Comparative Analysis: Points of Consensus and Conflict A comparative analysis of the literature highlights both strong consensus on key trends and points of disagreement or complexity. In the related literature, there is widespread agreement that the synergistic combination of a high-quality AIS and a robust, efficient internal audit unit exert a positive and substantial impact on the accuracy of financial reporting and the overall performance of banking institutions. Furthermore, a firm consensus has emerged among researchers that emerging technologies such as AI and BDA play a transformative role, paving the way for a fundamental paradigm shift toward proactive, continuous, and fully risk-based auditing. By enabling real-time monitoring, early anomaly detection, and predictive risk analysis, these technologies transform internal auditing from a reactive and periodic approach into a strategic and value-creating function, ultimately contributing significantly to strengthened corporate governance and sustained banking performance [ 12 ]. Nevertheless, the literature also reveals contentious points and areas of debate. One prominent and debated issue is the direct effectiveness of CAIS in fraud detection. While some studies assume a positive and direct impact of these systems on fraud prevention and detection, others demonstrate that a basic computerized AIS without support from advanced analytical tools has no significant effect on fraud detection in isolation. This effectiveness only becomes apparent when the system is combined with more advanced technologies such as BDA and a strong, efficient internal audit unit. These research discrepancies emphasize that mere automation and mechanized data recording are insufficient; rather, the true value of AIS in high-risk banking environments depends on its integration with intelligent analytical capabilities and precise human/organizational oversight. Such an approach not only elevates the system from a simple recording tool to a dynamic platform for internal control and proactive detection but also helps reduce false positives and enhance the accuracy of identifying genuine anomalies. These findings indicate that pure automation and process mechanization alone are inadequate; instead, leveraging advanced analytical intelligence and rigorous human/organizational supervision are key factors in achieving true effectiveness in audit systems [ 13 ]. Although the benefits of AI in auditing are widely praised, a growing body of research adopts a cautious perspective, focusing on serious implementation challenges such as algorithmic bias, the lack of unified standards, transparency issues, and ethical risks. These studies demonstrate that the path to integrating AI into audit processes is far from straightforward and obstacle-free, requiring a systematic approach to technology governance and risk management. Ultimately, despite the growing enthusiasm for the concept of integrated auditing, its absolute and universal superiority over separated auditing remains questionable and may depend on specific institutional factors, cultural-legal differences, and measurement variables as evidenced by the diverse and sometimes conflicting findings in the existing literature. This variety of opinions underscores the need for further comparative research and greater attention to contextual factors in evaluating audit approaches. In conclusion, the integration of internal auditing into AIS frameworks for real-time monitoring of bank balances represents a dynamic, strategic, and vital domain for the transformation and advancement of the banking industry. The existing literature clearly points toward a future in which auditing will be fully continuous, data-driven, and intelligent leveraging the capabilities of AI, big data analytics, and predictive monitoring to proactively manage risk and ensure sustainable value creation [ 14 ]. However, fully realizing this vision requires overcoming existing challenges. Future research should focus on addressing the identified gaps, including the development of valid conceptual and empirical models tailored to specific contexts for real-time monitoring, the formulation of globally accepted standards for AI governance in banking auditing, and deeper examination of the practical, technical, and ethical challenges of this technological transformation (such as algorithmic bias, model transparency, data security, and accountability). Prioritizing these areas will not only advance the scientific progress of the field but also provide the necessary foundation for the successful and responsible implementation of CA in the era of digital banking, ultimately contributing to enhanced sustainability, transparency, and trust in the global banking system [ 15 ]. 3. Research methodology This research is applied in purpose and descriptive-correlational in terms of data collection method, adopting a mixed-methods approach. The research design is based on a convergent parallel mixed-methods design, in which quantitative and qualitative data are collected and analyzed concurrently, with triangulation applied during the interpretation phase to integrate the results. This approach facilitates a more comprehensive and multidimensional understanding of the phenomenon under study and enhances the internal validity of the research through the mutual complementarity of quantitative results (with statistical precision) and qualitative findings (with explanatory depth). The study population comprises professionals employed in Iran's banking and financial sector (internal auditors, financial managers, information technology experts, and researchers in the finance/technology domain). Sampling for the quantitative component was conducted using a convenience random method, yielding 260 completed questionnaires. For the qualitative component, semi-structured interviews were carried out with 15 key experts (selected purposively based on criteria of expertise and practical experience in internal auditing and accounting information systems) to provide deeper insights. 3.1 Analysis and Data Collection Tools Quantitative component : The research was conducted using a survey method, with the primary data collection tool being a structured questionnaire designed based on the key research dimensions (AIS implementation and adoption, real-time monitoring of account balances, data accuracy and quality, system integration, auditor skills, information security, internal audit effectiveness, and data transparency). The questionnaire items were developed drawing on existing literature [ 1 ][ 16 ], employed a five-point Likert scale (ranging from "strongly disagree" to "strongly agree"). Following the collection of usable questionnaires, the Kolmogorov-Smirnov test was initially performed to assess the normality of the data distribution, the results of which confirmed non-normality. Consequently, non-parametric tests were utilized. Relationships among variables were examined using Spearman's correlation test. Additionally, the moderating role of variables such as auditor skills and process integration was evaluated through hierarchical regression. Statistical analyses were performed using SPSS software. Qualitative component : Semi-structured interviews were conducted using an interview guide comprising open-ended questions on the benefits, challenges, and recommendations for implementing the integration of internal auditing into AIS. The interview data were analyzed using Braun and Clarke’s (2006) thematic analysis method, which involved stages of familiarization with the data, initial coding, theme extraction, review, and final theme definition. Over 830 meaningful statements were extracted and grouped into primary and secondary themes. Integration : Quantitative and qualitative results were triangulated during the interpretation phase to provide deeper explanations for both confirmed and rejected findings. The content validity of the questionnaire was confirmed through expert judgment from 8 specialists in auditing and information systems (CVR exceeding 0.79 for most items). Construct validity was assessed via confirmatory factor analysis. Reliability was established by calculating Cronbach's alpha, yielding 0.87 for the overall questionnaire and values ranging from 0.78 to 0.91 for individual dimensions, indicating acceptable reliability. In the qualitative component, credibility was ensured through source triangulation and member checking. 4. Research Hypotheses 4.1 Descriptive statistics of research variables Demographic Information. The frequency and frequency percentage are as follows. Respondent's side Frequency Percent Valid Percent Cumulative Percent Valid Researcher (Finance/Technology) 37 14.2 14.2 14.2 Financial Institutions / Banks 99 38.1 38.1 52.3 Auditor 67 25.8 25.8 78.1 Information Technology 30 11.5 11.5 89.6 Chief Financial Officer 27 10.4 10.4 100.0 Total 260 100.0 100.0 Type of Company Activity Frequency Percent Valid Percent Cumulative Percent Valid Commerce 26 10.0 10.0 10.0 productive 62 23.8 23.8 33.8 Service 110 42.3 42.3 76.2 Other 44 16.9 16.9 93.1 Technology 18 6.9 6.9 100.0 Total 260 100.0 100.0 Number of Employees Frequency Percent Valid Percent Cumulative Percent Valid More than 50 people 18 6.9 6.9 6.9 Between 10 and 50 people 119 45.8 45.8 52.7 Less than 10 people 123 47.3 47.3 100.0 Total 260 100.0 100.0 Company Activity History Frequency Percent Valid Percent Cumulative Percent Valid More than 10 years 53 20.4 20.4 20.4 Between 3 and 10 years 108 41.5 41.5 61.9 Less than 3 years 99 38.1 38.1 100.0 Total 260 100.0 100.0 Research Variables. The mean and standard deviation of the research variables are as follows. Statistics N Mean Std. Deviation Valid Missing Implementation and Acceptance of Internal Audit in AIS 260 0 3.640 0.400 Real-time monitoring of bank account balances 260 0 3.640 0.385 Data accuracy and quality 260 0 3.703 0.354 Integration of the internal audit system with other departments 260 0 3.520 0.396 Auditors' Skills and Expertise 260 0 3.365 1.014 Security and confidentiality of banking information 260 0 3.395 0.443 Effectiveness and Value of Internal Audit 260 0 3.728 0.312 Consequences and User Satisfaction 260 0 3.417 0.432 Transparency and reliability of banking data 260 0 3.611 0.287 The method of data distribution was investigated using Kolmogorov-Smirnov test and the results showed that the data distribution was not normal, so non-parametric tests were used. Tests of Normality Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Implementation and Acceptance of Internal Audit in AIS .105 260 .000 .979 260 .001 Real-time monitoring of bank account balances .091 260 .000 .983 260 .003 Data accuracy and quality .095 260 .000 .977 260 .000 Integration of the internal audit system with other departments .095 260 .000 .976 260 .000 Auditors' Skills and Expertise .207 260 .000 .904 260 .000 Security and confidentiality of banking information .094 260 .000 .983 260 .003 Effectiveness and Value of Internal Audit .102 260 .000 .973 260 .000 Consequences and User Satisfaction .107 260 .000 .980 260 .001 Transparency and reliability of banking data .100 260 .000 .974 260 .000 a. Lilliefors Significance Correction Main Hypothesis: The integration of internal auditing into AIS frameworks enhances the effectiveness and accuracy of real-time monitoring of bank balances. The quantitative results of the study indicated that internal audit integration into AIS generally improves the effectiveness and accuracy of real-time monitoring, as certain subsidiary hypotheses such as the positive relationship with reporting accuracy and the moderating roles of auditor skills and process integration were supported. However, others, including increased speed of error detection and improved transparency, were rejected. Qualitative findings from the thematic analysis of interviewees' responses complement these results. For instance, the primary theme of "Positive Effects of Integration" included sub-themes such as "Real-time data analysis and faster decision-making" (14% frequency) and "Enhanced review efficiency and faster error detection" (14%), demonstrating improved monitoring effectiveness. In contrast, challenges such as "Weaknesses in software infrastructure and security concerns" (6%) explain why certain aspects, like transparency, were not supported in the quantitative results. Furthermore, qualitative recommendations such as "Managerial support and development of unified standards" (7%) emphasize that strengthening these factors could further enhance the overall effectiveness of the integration and improve the accuracy of real-time monitoring of bank balances. 4.2 Inferential statistics Sub-hypothesis 1: The integration of internal audit into the AIS has a significant positive relationship with the accuracy of real-time bank balance reports. To evaluate this hypothesis, Spearman's correlation test was used. A significance value less than 0.05 indicates that there is a significant relationship between the integration of internal audit in AIS and the accuracy of real-time bank balance reports. Correlations Implementation and Acceptance of Internal Audit in AIS Data accuracy and quality Spearman's rho Implementation and Acceptance of Internal Audit in AIS Correlation Coefficient 1.000 0.153 * Sig. (2-tailed) . 0.013 N 260 260 Data accuracy and quality Correlation Coefficient 0.153 * 1.000 Sig. (2-tailed) 0.013 . N 260 260 Correlation is significant at the 0.05 level (2-tailed). results using Spearman's correlation test showed that there was a positive and significant relationship between the integration of internal audit into the AIS and the accuracy of real-time reports, which confirms this hypothesis. The qualitative findings complement this result with the theme "Positive effects of integration" and the sub-theme "Increasing transparency and reducing manual errors" (with a frequency of 11%), where interviewees emphasized "the use of integrated tools." has increased transparency and reduced manual errors in reporting", which directly refers to improving the accuracy of reports. Also, the theme "Synergy between units and improving information flow" (12%) suggests that integration leads to more accurate data, while challenges such as "lack of coordination between teams" (5%) may affect accuracy in certain cases, but reinforce the totality of quantitative findings. Sub-hypothesis 2: Integrating internal audit into the AIS increases the speed of detection of banking errors and anomalies in real time. To evaluate this hypothesis, Spearman's correlation test was used. A significance value greater than 0.05 indicates that the integration of internal audit into AIS does not increase the speed of detection of banking errors and anomalies instantaneously. Correlations Implementation and Acceptance of Internal Audit in AIS Real-time monitoring of bank account balances Spearman's rho Implementation and Acceptance of Internal Audit in AIS Correlation Coefficient 1.000 0.105 Sig. (2-tailed) . 0.092 N 260 260 Real-time monitoring of bank account balances Correlation Coefficient 0.105 1.000 Sig. (2-tailed) 0.092 . N 260 260 Quantitative results from Spearman's correlation indicated no significant relationship (r = 0.105, p = 0.092), leading to rejection of the hypothesis. This may be attributed to measurement limitations (e.g., perceptual scale sensitivity in a transitional context) or sample-specific factors such as legacy system prevalence in the studied banks. However, qualitative thematic analysis strongly supports the potential benefit, with the sub-theme "Increase the efficiency of checks and speed of error detection" (14% frequency) highlighting interviewees' views that "integration of audit and IT increased efficiency and speed of error detection." This complementarity suggests that while current infrastructure constrains observable effects, targeted technological upgrades could realize these gains. Sub-hypothesis 3: Integrating internal audit into the AIS increases the transparency and reliability of real-time banking data. To evaluate this hypothesis, Spearman's correlation test was used. A significance value greater than 0.05 indicates that the integration of internal audit into the AIS does not increase the transparency and reliability of banking data in real time. Correlations Implementation and Acceptance of Internal Audit in AIS Transparency and reliability of banking data Spearman's rho Implementation and Acceptance of Internal Audit in AIS Correlation Coefficient 1.000 0.119 Sig. (2-tailed) . 0.055 N 260 260 Transparency and reliability of banking data Correlation Coefficient 0.119 1.000 Sig. (2-tailed) 0.055 . N 260 260 The quantitative results of Spearman's correlation test indicate that there is no significant relationship (correlation coefficient 0.119, p=0.055) between internal audit integration into AIS and increased transparency and reliability, which refutes the hypothesis. Qualitative findings complement this result by providing more in-depth explanations, the theme of "positive effects of integration" and the subtheme of "enhancement" Transparency and Reducing Manual Errors" (11%) supports the potential of integration, with quotes such as "The use of integrated tools has increased transparency and reduced manual errors in reporting", but challenges such as "security concerns" (6%) and "lack of coordination between teams" (5%) suggest why transparency has not increased in practice, and qualitative suggestions such as "Strengthening Data Security" (6%) suggest that by removing these barriers, The reliability of data in real-time can be improved. Sub-hypothesis 4: The skills and expertise of internal auditors play a moderating role in the effect of internal audit integration on instantaneous banking supervision. To evaluate this hypothesis, regression was used. The first table shows the value of the coefficient of determination, which explains the percentage of fit of the model. This value is equal to 16.3%. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.163a 0.027 0.019 0.381731048046937 a. Predictors: (Constant), Moderator 4, Implementation and Adoption of Internal Audit in AIS In the second table, the hypothesis is confirmed or rejected according to the significance value, given that the significance value is less than 0.05, so the hypothesis is confirmed. ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 1.029 2 0.514 3.529 0.031b Residual 37.450 257 0.146 Total 38.478 259 a. Dependent Variable: Real-time monitoring of bank account balances b. Predictors: (Constant), Modulator4, Implementation and Adoption of Internal Audit in AIS The following table shows the regression equation which is observed due to the significant value of the moderation role being greater than 0.05. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3.068 0.218 14.100 0.000 Implementation and Acceptance of Internal Audit in AIS 0.157 0.060 0.163 2.618 0.009 Modulator4 0.002 0.026 0.004 0.069 0.945 a. Dependent Variable: Real-time monitoring of bank account balances Hierarchical regression showed a significant overall model (R² = 0.027, F = 3.529, p = 0.031), with a positive main effect of integration (β = 0.163, p = 0.009). However, the moderation term for auditors' skills was non-significant (β = 0.004, p = 0.945), indicating no moderating role in this sample. This aligns with heterogeneous skill distribution and external constraints (e.g., limited training access). Qualitative findings from the "Integration Challenges" theme (sub-theme "Employee Resistance and Lack of Technical Skills", 5%) explain this: interviewees noted resistance and skill gaps as major barriers. The "Suggestions for Improvement" theme (sub-theme "Practical Training and Joint Workshops", 6%) proposes solutions, suggesting the moderating role could emerge under improved conditions. Thus, the rejection appears context-dependent rather than absolute. Sub-hypothesis 5: Integrating internal audit into AIS reduces the risks of fraud and misuse of bank balances in real time. To evaluate this hypothesis, Spearman's correlation test was used. A significance value greater than 0.05 indicates that the integration of internal audit into the AIS does not reduce the risks of fraud and misuse of bank balances in the moment. Correlations Implementation and Acceptance of Internal Audit in AIS Security and confidentiality of banking information Spearman's rho Implementation and Acceptance of Internal Audit in AIS Correlation Coefficient 1.000 0.076 Sig. (2-tailed) . 0.221 N 260 260 Security and confidentiality of banking information Correlation Coefficient 0.076 1.000 Sig. (2-tailed) 0.221 . N 260 260 The results of the quantitative Spearman correlation test showed no significant relationship (correlation coefficient 0.076, p=0.221) between the integration of internal audit into AIS and the reduction of fraud risks, which rejects the hypothesis. Qualitative findings corroborate this result with the theme "Integration challenges" and the sub-theme "Software Infrastructure Weakness and Concern “Security" (6%) supplements, explaining why the risk mitigation was not observed, with quotes such as "weak software infrastructure and data security concerns have made full implementation difficult." However, the theme of "Suggestions for Improvement" such as "Creating an Intermediate Team and Strengthening Data Security" (6%) suggests that by focusing on security, integration can reduce fraud risks in the future and offset quantitative findings. Sub-hypothesis 6: The degree of integration of the organization's financial and control processes determines the intensity of the impact of the integration of internal audit into the AIS on the real-time monitoring of bank balances. Regression was used to evaluate this hypothesis. The first table shows the value of the coefficient of determination, which explains the percentage of fit of the model. This is 25.9 %. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.259a 0.067 0.060 0.373727446845992 a. Predictors: (Constant), Modulator6, Implementation and Adoption of Internal Audit in AIS In the second table, the hypothesis is confirmed or rejected according to the significance value, given that the significance value is less than 0.05, so the hypothesis is confirmed. ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 2.582 2 1.291 9.245 0.000b Residual 35.896 257 0.140 Total 38.478 259 a. Dependent Variable: Real-time monitoring of bank account balances b. Predictors: (Constant), Modulator6, Implementation and Adoption of Internal Audit in AIS The following table shows the regression equation which is observed due to the significant value of the moderation role being greater than 0.05. Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3.203 0.216 14.815 0.000 Implementation and Acceptance of Internal Audit in AIS 0.118 0.059 0.123 1.994 0.047 Modulator6 0.063 0.019 0.205 3.336 0.001 Real-time monitoring of bank account balances = 3.203 + 0.118 × Implementation and adoption of internal audit in AIS + 0.063 × Degree of integration of financial and control processes in the organization a. Dependent Variable: Real-time monitoring of bank account balances The degree of integration of the organization's financial and control processes determines the intensity of the impact of internal audit integration in the AIS on the real-time monitoring of bank balances. The quantitative results of regression indicate the moderating role of integrity (R²=0.067, p=0.000 for the overall model, p=0.001 for the modifier), which confirms the hypothesis. The qualitative findings corroborate this result with the theme "Positive effects of integration" and the sub-theme "Synergy between units and flow improvement." Information" (12%) completes, where interviewees stated that "this merger has led to synergy between finance and technology units and improved information flow", which directly points to the severity of the integration impact. Also, the challenge of "lack of coordination between teams and lack of integrated standards" (5%) explains how a lack of integration can reduce the impact of integration, and the proposal for "developing integrated tools and pilot projects" (6%) offers a solution to reinforce this moderating role. 4.3 Thematic Analysis Interview Responses: The responses of the interviewees were analyzed using Brown and Clark (2006) thematic analysis method. After open coding, more than 830 meaningful sentences/phrases were extracted and grouped into main themes. The main themes are divided into 4 categories: Final Table of Main and Sub-Themes Main Theme substrate Approximate frequency (of total phrases) Direct Quote Example Positive Effects of Integration Real-time data analysis and faster decision-making 119 (14%) "Integrating processes has enabled real-time data analysis and faster management decision-making." Increase the efficiency of checks and speed of error detection 117 (14%) "The integration of audit and information technology has increased the efficiency of investigations and increased the speed of error detection." Synergy between units and improved information flow 101 (12%) "This integration has led to synergies between the financial and technology units and improved the flow of information." Increasing transparency and reducing manual errors 93 (11%) "The use of integrated tools has increased transparency and reduced manual errors in reporting." Integration Challenges Weak software infrastructure and security concerns 53 (6%) "Weak software infrastructure and data security concerns have made full implementation difficult." Implementation costs and the need for specialized training 49 (6%) "Implementation and maintenance costs, as well as the need for specialized training, are a barrier for small businesses." Lack of coordination between teams and lack of integrated standards 45 (5%) "The lack of coordination between audit and IT teams and the lack of unified standards are challenging." Employee Resistance and Lack of Technical Skills 43 (5%) "Staff resistance to change and lack of technical skills are among the most important obstacles." Suggestions for improvement Management support and development of integrated standards 56 (7%) "He emphasizes that managerial support and the development of integrated standards are essential for success." Hands-on training and joint workshops 51 (6%) "It is recommended that practical training and joint workshops be held between audit and IT." Build an interdepartmental team and strengthen data security 51 (6%) "Creating an interdepartmental team and strengthening data security is an effective solution." Development of integrated tools and pilot projects 47 (6%) "It proposes the development of integrated tools and the launch of pilot projects prior to implementation at the organizational level." Interaction between departments Automatic synchronization and increased process efficiency 5 (1%) "With the implementation of this integration, there will be an automatic synchronization between different departments of the organization, and the system will increase the efficiency of accounting and banking processes." 4.4 Complementing the results of hypotheses with qualitative findings The mixed-methods approach employed in this study, by enabling triangulation between quantitative and qualitative findings, led to a deeper and more multifaceted understanding of the phenomenon of integrating internal auditing into AIS frameworks. The quantitative results provided partial support for the main hypothesis, demonstrating a positive and significant relationship between internal audit integration and financial reporting accuracy, as well as confirming the moderating roles of auditor expertise and the degree of organizational process integration. However, no significant relationships were found for variables such as the speed of anomaly detection, data transparency, or direct reduction in fraud risk. Qualitative findings, derived from thematic analysis of semi-structured interviews, served to explain and enrich the quantitative results. The primary theme of "Positive Effects of Integration" encompassing sub-themes such as real-time data analysis, enhanced efficiency of review processes, inter-unit synergy, and reduction of manual errors highlighted the substantial potential of this integration within AIS frameworks. These findings indicate that in organizations with adequate technological infrastructure and sufficient managerial support, outcomes such as faster decision-making and improved information flow have been tangibly realized. These qualitative insights effectively account for why certain quantitatively supported hypotheses including improved reporting accuracy and the moderating role of process integration were also observable in practice. Conversely, the theme of "Integration Challenges" including issues such as weaknesses in software infrastructure, security concerns, employee resistance to change, shortages of specialized technical skills, and lack of effective inter-team coordination clearly elucidates the reasons for the non-confirmation of certain subsidiary hypotheses. For example, despite the theoretical potential of integration to increase anomaly detection speed and reduce fraud risk, practical limitations such as dependence on legacy systems and skills gaps hindered the full realization of these benefits in the banks studied. Furthermore, the theme of "Recommendations for Improvement" emphasizes the necessity of practical and ongoing training, strengthened data security mechanisms, increased managerial support, and the development of integrated platforms actions that could address the shortcomings observed in the quantitative results in future research and implementations, thereby enabling more efficient exploitation of the integration's benefits. Ultimately, the triangulation of findings reveals that the integration of internal auditing into AIS is, from a conceptual and potential standpoint, a highly effective approach. However, fully realizing its benefits is substantially dependent on overcoming organizational, technical, and human barriers. This convergence and mutual complementarity of quantitative and qualitative results not only strengthened the internal validity of the study but also compared to single-method studies delivered more practical and realistic insights for policymakers, managers, and researchers in the fields of auditing and information systems. 5. Discussion and Analysis The findings of this study provide partial empirical support for the integration of internal audit functions into Accounting Information Systems (AIS) frameworks as a mechanism to enhance real-time monitoring of bank balances. Consistent with agency theory, the positive relationship between AIS-internal audit integration and financial reporting accuracy (sub-hypothesis 1) confirms that such integration can reduce information asymmetry and improve data reliability a result aligned with prior studies in banking contexts [ 1 ]. Similarly, the significant moderating role of organizational process integration (sub-hypothesis 6) underscores the importance of cohesive financial and control processes in amplifying the benefits of integration, echoing the knowledge spillover effect observed in integrated auditing literature (e.g., integrated auditing superiority in Chinese listed firms) [ 16 ]. However, the non-significant results for sub-hypotheses 2 (anomaly detection speed), 3 (data transparency), and 5 (fraud risk reduction) indicate that the anticipated benefits do not always materialize uniformly in practice. These findings are consistent with documented implementation challenges in the literature, including legacy system constraints, data volume overload, and insufficient technological maturity (Ghadeer, 2022; Proceedings of the 8th International Conference on Big Data and Computing, 2023). The low R² values in the regression models (0.027 for auditor skills moderation and 0.067 for process integration moderation) further suggest that a substantial portion of variance in real-time monitoring effectiveness remains unexplained, likely due to unmeasured contextual factors such as regulatory environment, organizational culture, and stage of digital transformation in the sampled Iranian banks. The convergent mixed-methods design proved particularly valuable in interpreting these discrepancies. While quantitative analyses constrained by perceptual scales and non-normal data distributions showed limited or no significance for several outcomes, the thematic analysis of expert interviews revealed substantial perceived potential. High-frequency positive sub-themes (e.g., “real-time data analysis and faster decision-making” at 14%, “increased efficiency of checks” at 14%, “synergy between units” at 12%) indicate that practitioners strongly believe in the transformative capacity of integration when infrastructural and human barriers are addressed. Conversely, prominent challenge themes (e.g., “weak software infrastructure and security concerns” at 6%, “employee resistance and lack of technical skills” at 5%) provide a plausible explanation for the non-significant quantitative results: current organizational and technological conditions in the studied context appear to suppress observable effects. From a theoretical perspective, these results enrich agency theory by illustrating that effective internal audit integration can mitigate agency costs through enhanced real-time oversight, but only when supported by adequate structural alignment (process integration). The non-significant moderating role of auditors’ skills (sub-hypothesis 4) suggests that, in transitional settings such as emerging markets, individual expertise alone may be insufficient without systemic training and cultural change a finding that aligns with calls for new competencies in AI-augmented auditing (Kuna, 2025; Olorunyomi et al., 2022).[ 12 ]. Practically, the study highlights actionable implications for banking institutions in resource-constrained environments: prioritizing investments in interoperable AIS platforms, cross-functional training programs, and pilot implementations could unlock the latent benefits identified qualitatively. For regulators and standard-setters, the results reinforce the need for updated governance frameworks addressing algorithmic transparency, bias mitigation, and ethical AI use in continuous auditing [ 11 ]. 5.1 Research Limitations Despite efforts to provide a comprehensive perspective on the application of privacy-preserving technologies in AIS within the Iranian banking sector, this research encountered several methodological and contextual limitations that partially affect the generalizability and depth of its findings. First, the convenience sampling approach and focus on Iranian banking professionals limit generalizability to other contexts or more mature digital banking ecosystems. Second, reliance on self-reported perceptual data may introduce common method bias or social desirability effects, particularly regarding sensitive topics such as fraud detection and security. Third, the relatively low explained variance (R² < 0.07 in moderation models) indicates that important predictors (e.g., specific technology adoption stage, organizational size effects, or external regulatory pressure) were not captured. Finally, while triangulation strengthened interpretive depth, the modest qualitative sample size (n = 15) may not fully represent the diversity of perspectives across Iran’s heterogeneous banking sector. Sampling was conducted using a non-probability method, with the study population primarily limited to professionals employed in banks, financial institutions, the information technology sector in Iran. Although this approach facilitated access to relevant experts, it increases the risk of selection bias and necessitates caution in generalizing the results to the entire population of banking professionals in the country or to other developing nations. Reliance on self-reported data through questionnaires and semi-structured interviews introduces the potential for social desirability bias, particularly in a sensitive topic such as data privacy and security, where respondents may tend to align their views with socially or professionally desirable responses. The research focused on experts' perceptions and opinions, lacking an examination of the actual implementation of privacy-preserving techniques (such as homomorphic encryption or federated learning) in operational environments of Iranian banks. This limitation stems from difficulties in accessing real banking systems due to organizational security and confidentiality considerations, resulting in findings that are more descriptive and perceptual than empirical and operational. The absence of a comprehensive and transparent legal framework in Iran for personal data protection (comparable to GDPR in Europe), coupled with restrictions on access to sensitive banking information, constrained the potential for deeper empirical studies or analysis of real transactional data. The qualitative sample size (25 interviews), although proceeding until theoretical saturation was achieved, was limited relative to the broad diversity of financial organizations in Iran, potentially leaving some marginal or regional perspectives uncovered. 5.2 Directions for Future Research Future studies could employ longitudinal designs to track the evolution of integration benefits over time, incorporate objective performance metrics (e.g., actual anomaly detection rates or fraud incident logs), or apply advanced robust statistical techniques (e.g., bootstrapped regression) to address non-normality. Comparative cross-country research, particularly between emerging and developed markets, would further clarify contextual moderators. Additionally, experimental or simulation-based studies examining AI governance mechanisms in real-time auditing could provide deeper insights into overcoming current barriers. 6. Conclusion This research was conducted at a sensitive and pivotal time, when the banking sector is confronting rapid digital transformation, escalating cyber threats and risks, and increasingly stringent regulatory requirements. The findings demonstrate that the integration of internal auditing into AIS frameworks holds substantial potential for enhancing real-time monitoring of bank balances, thereby improving reporting accuracy, enabling proactive risk management, and strengthening stakeholder confidence. Despite the identified practical challenges including weaknesses in infrastructure and skills gaps that currently hinder the full realization of these benefits triangulation of quantitative and qualitative results indicates that these barriers can be overcome through targeted investments in technology, training, governance frameworks. This study not only fills existing gaps in the scholarly literature and contributes to advancing the theoretical understanding of CA in digital banking but also provides valuable practical insights for banking professionals, internal auditors, information systems designers, and regulatory authorities. Ultimately, the successful integration of internal auditing into AIS transcends mere technological advancement; it emerges as a strategic imperative for sustaining the resilience and efficiency of the banking sector in an era of instantaneous transactions and complex risks. 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Int J Frontline Res Multidisciplinary Stud 1(2):94–112. https://doi.org/10.56355/ijfrms.2022.1.2.0057 The Influence of Accounting Information Systems on Internal Audit Effectiveness in Hasanuddin University Nalendra Bhayu Permana and Andi Kusumawati Hasanuddin University, Makassar, Indonesia https://doi.org/10.2991/978-94-6463-758-8_198 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9709682","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":640049776,"identity":"9d619423-b67f-4712-a898-dedc70cbbc16","order_by":0,"name":"Saeed Askari Nia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBACNhReAgODHIg+8IAILRIwLcZgLQlE2CYBYyQ2QPXiBHzsx589YKi4U8cvffbYhwd/bNLnhx1+CLTFTk63AYfDeHLMDRjOPJOQ7MtLnpHYlpa78XaaAVBLsrHZAVx+yWGTYGw7LGFwhscY6KrDuRtnJ4C0HEjchksL//NnEoz/DkvYg7Qk/Pmfbjg7/QN+LRIJZhKMDUBbeEBa2A4kyEvnELBF4o25QcKxw5IzzvAlMyS2JRtukM4pOJBggNsv8v3pzx58qDnMz9/De5jxxx87efnZ6Zs/fKiwk8OlBRwCCWCaB8I1AKs0wKkcooUBWYt8A17Vo2AUjIJRMAIBAJXdXBZRv1/vAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-5893-928X","institution":"Islamic Azad University","correspondingAuthor":true,"prefix":"","firstName":"Saeed","middleName":"Askari","lastName":"Nia","suffix":""}],"badges":[],"createdAt":"2026-05-14 04:42:24","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9709682/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9709682/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109761007,"identity":"fc1789f8-0c67-4b4b-93b6-aedfc965eab4","added_by":"auto","created_at":"2026-05-22 07:29:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":529195,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9709682/v1/a7c86650-066d-439e-8726-4b94a4991b3a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eReal-Time Integration of Internal Auditing into AIS Frameworks for Monitoring Bank Balances\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe rapid digitization of financial services has rendered strong, real-time oversight and proactive risk management indispensable for modern banking institutions. Accounting Information Systems (AIS) serve as the infrastructural backbone, playing a pivotal role in processing, recording, and reporting financial transactions, thereby enhancing operational efficiency and regulatory compliance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe seamless integration of internal audit functions into AIS architectures represents a strategic advancement, particularly in an environment where the banking sector faces an escalating need for instantaneous monitoring of account balances to prevent fraud, optimize liquidity, and align with a dynamic and evolving regulatory landscape [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite remarkable technological progress, persistent challenges such as massive data volumes, limitations of legacy systems, and continued reliance on periodic audits have impeded the realization of real-time assurance. Existing literature has largely examined AIS and internal auditing in isolation, with limited attention to their synergies in the context of continuous monitoring of bank balances [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study addresses these gaps through an innovative mixed-methods approach, emphasizing the transformative role of artificial intelligence while prominently considering its governance implications, including algorithmic bias, transparency, and ethical concerns. This convergent mixed-methods design, which interweaves robust statistical analyses with deep qualitative insights, remains relatively rare in the digital banking domain and contributes to the development of effective corporate governance frameworks for the application of AI in continuous auditing (CA) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Importance of the research topic\u003c/h2\u003e \u003cp\u003eBanks operate in a highly competitive environment characterized by stringent regulations, where high transparency, rapid decision-making, and continuous regulatory compliance are critical imperatives. Internal auditing, which was traditionally viewed primarily as an assurance-oriented and backward-looking activity, has evolved into a dynamic and well-established function within corporate governance. This function plays a pivotal role in ensuring the effectiveness of internal controls and the reliability of real-time data generated by modern Accounting Information Systems (AIS) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReal-time monitoring of bank balances enabled by technological advancements such as open banking APIs, automated transaction reconciliation, and high-speed data processing has now become essential and indispensable for effective liquidity management, early fraud detection, and accurate, timely reporting to regulatory authorities [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrior research indicates that AIS significantly enhances the efficiency and quality of internal audit processes by automating routine data collection and processing tasks, improving the timeliness and accuracy of audit reporting, and strengthening risk assessment capabilities.\u003c/p\u003e \u003cp\u003eMore recent studies emphasize that the synergistic integration of AIS with internal audit functions not only assists banking institutions in meeting regulatory requirements but also contributes to optimizing overall organizational performance and reinforcing corporate governance structures [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor example:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFocusing on the importance of integrating internal audit in the realization of the added value of information systems, Alassuli has confirmed the positive moderating effect of this function on the relationship between AIS implementation and the performance of banks.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eResearch such as Steavan et al. and Alzban have shown the prominent role of AIS in preventing fraud and reducing operational errors, especially when these systems are complemented and synergized with robust internal audit mechanisms.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAhmed et al. (2021) and Wen et al. (2019) have pointed to the increasing use of AI tools in AIS and emphasized the transformative potential of these technologies in achieving automated financial assurance and real-time monitoring.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eDespite significant advances in technology, several challenges remain in the way of integrating AIS with internal audit functions for real-time monitoring:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTechnical and infrastructural complexities: Large volumes of data (big data), limitations of legacy systems, and integration issues that make it difficult to integrate audit tools seamlessly and often lead to the generation of false positives or inability to process in real-time.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eContinuity of traditional approaches: Many banks continue to rely on periodic and post-incident audits, which are incompatible with the high speed of transactions and the dynamic risks of digital banking, and miss opportunities for early detection.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLimited focus of previous research: The available literature has often examined AIS and internal audit separately, and few studies have addressed their synergies in real-time frameworks, particularly bank balance monitoring, liquidity management, and regulatory compliance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGaps in standards and practices: Lack of standard frameworks for auditing AI-based systems, practical implementation mechanisms, best practices for ongoing assurance, as well as ethical issues such as algorithmic bias, transparency, and data privacy.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese challenges and gaps, despite the high potential of new technologies, have seriously hindered the full realization of continuous and preventive monitoring and require further empirical research and the development of governance standards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Research logic\u003c/h2\u003e \u003cp\u003eConsidering these gaps and high risks caused by inadequate supervision such as fraud, regulatory fines, and damage to organizational reputation, the present study was conducted with the aim of systematically investigating how to optimally integrate internal audit into AIS architectures for real-time monitoring of bank balances. This study seeks to answer the following key questions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHow does the integration of internal audit into AIS promote real-time monitoring of bank balances?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat role do moderating factors (such as auditors' expertise and process integrity) as well as organizational, technological, and supervisory factors influence this merger?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat implications does this merger have for the dominance of AI in banking auditing?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo answer these questions, the main hypothesis of the research: the integration of internal audit into accounting information systems (AIS) frameworks increases the effectiveness and accuracy of real-time monitoring of bank balances.\u003c/p\u003e \u003cp\u003eThe sub-hypotheses include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe integration of internal audit into the AIS has a significant positive relationship with the accuracy of real-time bank balance reports.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIntegrating internal audit into the AIS increases the speed of detection of banking errors and anomalies in real time.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIntegrating internal audit into the AIS increases the transparency and reliability of real-time banking data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe skills and expertise of internal auditors play a positive moderating role in the effect of merger on real-time monitoring.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIntegrating internal audit into AIS reduces the risks of fraud and misuse of bank balances in real time.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe degree of integration of the organization's financial and control processes moderates the intensity of the impact of the merger on real-time monitoring.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eBy bridging the gaps between audit theory and the procedures of modern information systems, this research will contribute to a comprehensive understanding of the following aspects:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe effectiveness and challenges of integrating internal audit with AIS for real-time monitoring.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe role of automation, artificial intelligence, and open banking technologies in facilitating real-time internal audit activities.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBest practices to achieve reliable and consistent assurance in digital banking environments.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis research has been conducted at a critical and pivotal juncture, holding substantial importance for both scientific and practical advancements. It offers valuable insights to banking professionals, internal auditors, information systems designers, and regulatory authorities. The ultimate goal is to bridge the identified gaps in the literature through an integrated and interdisciplinary approach to real-time banking risk management and control an approach that appears increasingly indispensable in an era characterized by instantaneous transaction flows and intricate regulatory demands. The present study employs a convergent mixed-methods design, the primary innovation of which lies in leveraging triangulation of quantitative and qualitative findings to achieve a more comprehensive and multidimensional understanding of the phenomenon under investigation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eQuantitative component: Data were collected using a structured questionnaire designed based on the key research dimensions (AIS integration, real-time monitoring, data accuracy and quality, system integration, information security, and internal audit effectiveness) from professionals employed in the banking sector. The Kolmogorov-Smirnov test results indicated non-normality of the data distribution; consequently, non-parametric tests were employed: Spearman correlation to examine relationships among variables and hierarchical regression to evaluate moderating effects.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eQualitative component: Semi-structured interviews were conducted with key experts, and the resulting data were analyzed using Braun and Clarke\u0026rsquo;s (2006) thematic analysis method. In this process, over 830 meaningful statements were extracted and grouped into primary and secondary themes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eResearch hypotheses: These include one main hypothesis (integration of internal audit into AIS enhances the effectiveness and accuracy of real-time monitoring of bank balances) and six subsidiary hypotheses (relationship with reporting accuracy, increased speed of anomaly detection, improved transparency, moderating role of auditor expertise, reduced fraud risk, and moderating role of organizational process integration).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMethodological innovation: The adopted mixed-methods approach enabled deeper explanation and interpretation of quantitative results particularly the rejected hypotheses through qualitative insights, leading to the identification of significant practical implications, especially in the domain of AI governance (such as ethical challenges, algorithmic bias, and the need for transparency). This triangulation not only strengthened the internal validity of the study but also facilitated the development of more practical and realistic recommendations.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe theoretical framework of this research is grounded in the principles of agency theory and the corporate governance literature. According to agency theory, information asymmetry between managers (agents) and stakeholders (principals) creates opportunities for opportunistic behavior and heightens the risk of financial fraud. The integration of internal audit functions into AIS frameworks by providing real-time data, enhancing transaction transparency, and strengthening traceability can mitigate this asymmetry and significantly reduce agency costs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a corporate governance perspective, internal auditing constitutes a core pillar of the internal control system and a key mechanism for supporting the audit committee and board of directors in monitoring financial and operational risks. In the digital banking environment, where transaction volume and velocity have increased dramatically, reliance solely on traditional periodic audits is insufficient. Transitioning toward CA based on AIS and emerging technologies such as artificial intelligence and big data analytics represents a strategic imperative [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDrawing further inspiration from contemporary approaches to data governance and AI governance, this research views the integration of internal auditing and AIS not merely as a technical tool but as a governance mechanism for ensuring transparency, accountability, and organizational-level risk management. When supported by adequate infrastructure and sufficient specialized skills, this mechanism can substantially enhance the quality of financial reporting and the effectiveness of monitoring bank balances.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Review of the literature","content":"\u003cp\u003eBased on a comprehensive literature review, the integration of internal auditing within AIS frameworks for real-time monitoring of bank balances represents a dynamic and rapidly expanding field, primarily driven by technological innovations. The central role of this integration lies in transforming internal auditing from a traditional, periodic, and reactive function into a continuous, proactive, and predictive process [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKey findings from the literature indicate that advanced technologies such as AI, big data analytics, and cloud computing are the primary drivers of this transformation. By enabling real-time processing and analysis of massive datasets, continuous transaction monitoring, and early detection of anomalies and potential fraud, these technologies significantly enhance the accuracy, efficiency, and overall effectiveness of internal audit processes in the banking sector.\u003c/p\u003e \u003cp\u003eEstablished frameworks such as COBIT and COSO provide robust foundations for IT governance and internal controls. Meanwhile, emerging conceptual models for predictive auditing and AI assurance frameworks are taking shape to guide the secure and effective implementation of these advanced technologies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch methodologies in this domain are increasingly quantitative, employing sophisticated techniques such as partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms to validate and evaluate the effectiveness of integrated audit and AIS systems. These quantitative approaches enable the testing of complex inter-variable relationships and the prediction of performance in real-world banking environments.\u003c/p\u003e \u003cp\u003eNevertheless, the literature simultaneously highlights several notable gaps. One of the most significant is the scarcity of empirical and focused studies that specifically address the intricacies of real-time monitoring of bank balances as a distinct and independent function.\u003c/p\u003e \u003cp\u003eFurthermore, fundamental challenges persist, including the absence of accepted standards for auditing AI-based systems, risks of algorithmic bias, serious concerns regarding data quality and integrity, cybersecurity and privacy issues, and the need to develop new specialized competencies for internal auditors. Although there is broad consensus on the potential benefits of this integration, fully realizing its potential requires coordinated, multifaceted efforts to address these practical, technical, and ethical challenges. The present study, with its emphasis on empirical aspects and adoption of a mixed-methods approach, takes a step toward bridging these gaps [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The Evolving Role of Internal Audit in Real-Time Financial Supervision\u003c/h2\u003e \u003cp\u003eThe integration of internal audit functions into AIS frameworks represents a fundamental paradigmatic shift in financial oversight, particularly in response to the banking sector's critical need for real-time monitoring of account balances. This evolution is primarily driven by the increasing complexity of financial transactions, heightened regulatory requirements, and the transformative power of emerging technologies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eModern internal auditing has transcended its traditional role characterized by periodic reviews and a focus on regulatory compliance to become a strategic, continuous, and fully technology-driven process. The primary objective of this process is to deliver real-time assurance, proactive risk management, and advanced fraud detection [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe synergy between a robust AIS and advanced internal audit capabilities is essential for ensuring data integrity, information security, the overall reliability of financial reporting in banking institutions. This integration not only enhances operational efficiency but also provides a solid foundation for digital corporate governance in the era of modern banking [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The Fundamental Impact of AIS and Internal Audit on Banking Performance\u003c/h2\u003e \u003cp\u003eA well-designed and effectively implemented AIS constitutes the primary foundation for efficient management and accurate financial reporting in any banking institution. Numerous studies have consistently confirmed that AIS exerts a positive and substantial impact on the accuracy of financial reporting through process automation, reduction of human errors, assurance of data integrity and consistency, creation of transparent audit trails, and enabling timely reporting [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor instance, a study on commercial banks in Jordan demonstrated that successful AIS implementation is associated with significant improvements in banking performance indicators, particularly when internal auditing acts as a moderating factor that amplifies the positive effect of AIS on overall bank performance. These findings underscore the importance of synergistic integration between AIS and internal audit mechanisms, illustrating that the true value of such systems is maximized only when complemented by robust and effective internal oversight and controls [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA key insight from this research is its identification of internal auditing as a strong moderating variable that significantly enhances the positive impact of AIS implementation on overall bank performance. This finding highlights the strategic imperative for banks not only to adopt advanced AIS but also to deeply and cohesively integrate them with rigorous internal audit functions, thereby fully realizing operational, managerial, and strategic benefits.\u003c/p\u003e \u003cp\u003eAlthough a Computerized Accounting Information System (CAIS) provides the essential infrastructure for recording and processing financial data, its effectiveness in fraud detection is often limited particularly when used in isolation without advanced analytical support.\u003c/p\u003e \u003cp\u003eA 2023 study revealed that while big data analytics and internal auditing exert positive and significant effects on accounting fraud detection, a computerized accounting information system alone does not demonstrate a meaningful impact in this regard (Proceedings of the 8th International Conference on Big Data and Computing, 2023). This finding emphasizes that the true value of an AIS becomes evident only when augmented by advanced analytical capabilities (such as machine learning algorithms) and precise internal audit oversight. In such cases, the system evolves from a mere recording tool into a dynamic platform for internal control, risk analysis, and proactive fraud detection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Strategic shift to integrated, risk-based auditing\u003c/h2\u003e \u003cp\u003eThe field of internal auditing has undergone remarkable evolution, transforming from a basic, primarily operational financial oversight function into a strategic pillar of corporate governance one that encompasses not only financial monitoring but also the management of organizational culture, information technology challenges, and comprehensive organizational risks. This evolution has been particularly accelerated by global financial crises and the growing demands from stakeholders for greater transparency and heightened accountability. Two fundamental philosophical shifts characterize this new era of internal auditing:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMoving towards integrated auditing, in which internal controls and financial statements are reviewed simultaneously and synergistically;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eand the widespread adoption of the risk-based internal auditing (RBIA) approach, which focuses audit resources on high-risk areas and creates more added value for the organization.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese changes have transformed internal audit from a reactive and compliance-oriented role to a strategic partner in board and senior management decisions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Integrative Audit Excellence\u003c/h2\u003e \u003cp\u003eProfessional organizations and standard-setters are increasingly endorsing the integrated auditing approach, a method in which the audit of internal controls over financial reporting (ICFR) is performed concurrently and synergistically with the audit of financial statements (FS). This approach stands in contrast to separated auditing, where these two activities are conducted independently and without coordination.\u003c/p\u003e \u003cp\u003eEmpirical evidence from a study on publicly listed Chinese companies indicates that integrated auditing offers significant advantages, leading to improved financial reporting quality and enhanced audit process efficiency. The primary driver of this superiority is identified as the knowledge spillover effect, whereby insights and deep understanding gained from auditing internal controls directly enhance the effectiveness and efficiency of financial statement audits, and vice versa.\u003c/p\u003e \u003cp\u003eThis synergistic effect not only eliminates redundant and unnecessary procedures but also provides a more comprehensive and integrated view of the organization's control environment and overall financial health, ultimately contributing to strengthened corporate governance and greater assurance for stakeholders [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Centrality of Risk-Based Internal Audit (RBIA)\u003c/h2\u003e \u003cp\u003eOne of the foundational pillars of modern internal auditing in the banking sector is the RBIA approach. This approach shifts the focus from routine, compliance-oriented testing to the strategic and targeted allocation of audit resources based on a comprehensive and dynamic assessment of organizational risks. By prioritizing high-risk areas, RBIA ensures that the most critical domains receive the greatest attention and resources, thereby significantly enhancing the value-added and overall efficiency of internal audit performance.\u003c/p\u003e \u003cp\u003eSuccessful implementation of this approach is heavily influenced by key organizational factors, including sustained commitment from senior management to a risk management culture, the quality and comprehensiveness of risk management training programs for internal auditors, and the level of comprehensive leadership support for risk-based audit processes. Collectively, these factors facilitate the institutionalization of RBIA and transform it into an effective tool for strengthening corporate governance and proactive risk management in banks.\u003c/p\u003e \u003cp\u003eFrom a theoretical perspective, the RBIA approach is rooted in Agency Theory, which emphasizes the existence of information asymmetry between managers (agents) and shareholders (principals). The theory argues that robust oversight mechanisms such as effective internal auditing are essential for aligning managers' interests with those of shareholders, reducing opportunistic behaviors, and ultimately lowering agency costs.\u003c/p\u003e \u003cp\u003eIntegrating a realistic real-time monitoring system with a comprehensive, risk-based internal audit approach empowers banks to substantially reduce the risks of deception and financial misrepresentation, bolster stakeholder confidence, elevate overall organizational performance. This synergy not only supports proactive risk management but also positions internal auditing as a strategic partner in creating sustainable value and reinforcing corporate governance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Technology Transformation: Factors Affecting Real-Time Monitoring\u003c/h2\u003e \u003cp\u003eThe ability to perform real-time monitoring of bank balances is almost entirely dependent on the integration of advanced technologies into AIS and internal audit frameworks. These technologies go beyond mere tools, playing a pivotal role in redefining the capabilities of internal auditing. Collectively, they facilitate a fundamental shift from reactive auditing which identifies issues after they occur to proactive and predictive auditing, which anticipates and mitigates risks in real time.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBig Data Analytics\u003c/b\u003e (BDA): The high volume of transactions in modern banking gives rise to the phenomenon known as Big Data. BDA enables auditors to perform real-time analysis of vast and diverse datasets, examining the entire data population rather than relying on traditional sampling methods. The application of BDA in bank internal audits particularly when integrated with Internet of Things (IoT)-based \u0026ldquo;continuous auditing (CA)\u0026rdquo; systems makes instantaneous monitoring of financial and operational activities possible. This data-driven approach facilitates early identification of concealed fraud and compliance issues, substantially reducing the risk of financial misrepresentations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eArtificial Intelligence\u003c/b\u003e (AI): AI, encompassing machine learning (ML), natural language processing (NLP), and predictive analytics, is fundamentally transforming audit processes. AI-based tools can automate repetitive and time-consuming audit tasks, detect anomalies and complex patterns that may indicate fraud, and dramatically improve the overall accuracy and speed of the audit process. In the digital banking domain, AI-enhanced cloud infrastructure provides unparalleled capabilities for real-time data analysis, anomaly detection, and predictive risk modeling. Specifically, real-time fraud detection systems powered by AI designed tailored for the financial services industry significantly increase fraud identification accuracy while minimizing false positive rates [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCloud Computing\u003c/b\u003e: The adoption of cloud technology has introduced a new, more flexible model for financial auditing. Cloud platforms facilitate seamless data sharing and transfer, enabling remote and real-time monitoring of financial audit information without the need for extensive on-premises infrastructure [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBlockchain Technology\u003c/b\u003e: Although blockchain remains an emerging technology in this field, it holds considerable potential for enhancing security, transparency, and integrity in banking transactions. Its integration with AI can create a powerful system for real-time transaction monitoring, where the immutable nature of the blockchain ledger ensures data integrity,AI algorithms identify and flag fraudulent activities as they occur.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Key Theories, Frameworks, and Methods in Modern Auditing\u003c/h2\u003e \u003cp\u003eThe integration of internal auditing into AIS for real-time monitoring is guided by a combination of established theories, corporate governance frameworks, and innovative methodologies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eUnderlying Theories\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAgency\u003c/b\u003e: As previously discussed, this theory provides the rationale for oversight mechanisms such as internal auditing to monitor management actions and reduce information asymmetry between managers (agents) and stakeholders (principals).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInformation Economics\u003c/b\u003e: This theory frames the value of information, emphasizing how accurate, timely, and relevant data from an AIS can improve decision-making and market efficiency objectives central to real-time monitoring.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStructuration\u003c/b\u003e: This theory can be applied to understand the dynamic interaction between human auditors (agency) and technological/organizational structures (e.g., AIS and regulations), which mutually shape and are shaped by auditors' actions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCorporate Governance and Implementation Frameworks\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA variety of frameworks are employed to oversee, implement, and evaluate these integrated systems. The table below summarizes some of the key frameworks identified in the literature. These frameworks offer a structured approach to managing the complexities of modern IT environments, ensuring that the integration of internal auditing and AIS is both effective and compliant with established standards.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey Frameworks in the Literature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFramework\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey Focus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelevant Studies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOBIT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA comprehensive framework for the governance and management of enterprise IT.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProvides control objectives to reduce audit risks and ensure the reliability and security of electronic AIS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGhadeer (2022) 15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOSO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA framework for internal control, risk management, and fraud deterrence.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOffers guidance on establishing effective internal controls, which is a foundational element for internal audit effectiveness.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlawale et al. (2022) 20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredictive Audit Frameworks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConceptual models that integrate AI/ML for real-time financial risk management.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnables a shift from reactive to proactive auditing by using predictive analytics for anomaly detection and continuous monitoring.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlorunyomi et al. (2022) 13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuality Assurance for AI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtocols for managing the quality and integrity of AI algorithms used in finance.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFocuses on bias detection, regulatory compliance, real-time performance monitoring, and continuous model validation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKuna (2025) 19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eData Quality by Design\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA methodology (based on ISO/IEC 25012) for embedding data quality into IS development.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnsures foundational data integrity and reliability, which is critical for the accuracy of any real-time monitoring system.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuerra-Garc\u0026iacute;a et al. (2023) 21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Research methods in literature\u003c/h2\u003e \u003cp\u003eResearchers exploring this domain employ a diverse array of research methodologies, each addressing different dimensions of the phenomenon of integrating internal auditing with AIS. Quantitative methods, particularly Partial Least Squares Structural Equation Modeling (PLS-SEM), widely used to test complex relationships among variables such as AIS implementation quality, internal audit efficiency, organizational performance. This method enjoys high popularity in banking research due to its flexibility in handling smaller samples and non-normal data distributions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, bibliometric analysis is utilized to map the historical evolution of research, identify primary themes, and highlight technological transformations such as the shift from traditional auditing to AI-based auditing in the context of internal audit effectiveness. This approach enables the examination of citation networks, co-authorship patterns, and keywords, providing a comprehensive picture of scientific advancements in the field [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany studies have also focused on developing conceptual frameworks to propose new models for incorporating advanced data analytics and artificial intelligence technologies into audit processes. These models often serve as a foundation for future empirical research and contribute to a deeper understanding of the operational and governance mechanisms underlying this integration. The diversity of these methods from advanced quantitative analyses to qualitative approaches and bibliometrics reflects the growing maturity of the field and underscores the necessity of adopting multidisciplinary perspectives to address the complexities of digital banking [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Gaps and limitations identified in the literature\u003c/h2\u003e \u003cp\u003eDespite the rapid advancements and growing volume of research, the literature reveals several notable gaps and limitations that warrant further investigation:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLack of specific focus on real-time bank balance monitoring\u003c/b\u003e: A primary gap is the scarcity of studies that specifically examine the integration of internal auditing into AIS for real-time monitoring of bank balances as a distinct function. While many articles discuss real-time capabilities in the broader contexts of fraud detection or CA, the specific nuances, challenges, and audit procedures related to bank balance monitoring (e.g., ensuring liquidity, overseeing large transactions, and real-time reconciliation) remain largely underexplored.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAbsence of standardized AI auditing methods\u003c/b\u003e: The adoption of artificial intelligence in auditing has outpaced the development of frameworks and standardized methods for auditing AI systems themselves. There is an urgent need for consistent and verifiable standards for auditing AI bias, algorithmic transparency, and model validation to ensure the reliability and consistency of these systems.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNeed for more empirical evidence\u003c/b\u003e: Although numerous conceptual papers address the benefits of AI and BDA, there is a relative dearth of empirical research on the long-term effectiveness, feasibility, broader organizational impacts of these technologies in real-world banking environments. Additional longitudinal studies are required to move beyond theoretical advantages to practical, evidence-based outcomes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImplementation challenges\u003c/b\u003e: Practical barriers to implementation are frequently acknowledged but rarely examined in depth. These include managing data privacy and security in real-time systems, overcoming significant skills gaps among auditors who require training in data analytics and AI, and navigating the high costs and complexities of integrating new technologies into legacy AIS frameworks.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe human element\u003c/b\u003e: As automation increases, the evolving role of the human internal auditor requires further study. Research is needed on how auditors interact with AI-based systems, interpret (or override) data outputs, and address emerging ethical dilemmas arising from reduced human oversight.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Comparative Analysis: Points of Consensus and Conflict\u003c/h2\u003e \u003cp\u003eA comparative analysis of the literature highlights both strong consensus on key trends and points of disagreement or complexity. In the related literature, there is widespread agreement that the synergistic combination of a high-quality AIS and a robust, efficient internal audit unit exert a positive and substantial impact on the accuracy of financial reporting and the overall performance of banking institutions.\u003c/p\u003e \u003cp\u003eFurthermore, a firm consensus has emerged among researchers that emerging technologies such as AI and BDA play a transformative role, paving the way for a fundamental paradigm shift toward proactive, continuous, and fully risk-based auditing. By enabling real-time monitoring, early anomaly detection, and predictive risk analysis, these technologies transform internal auditing from a reactive and periodic approach into a strategic and value-creating function, ultimately contributing significantly to strengthened corporate governance and sustained banking performance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, the literature also reveals contentious points and areas of debate. One prominent and debated issue is the direct effectiveness of CAIS in fraud detection. While some studies assume a positive and direct impact of these systems on fraud prevention and detection, others demonstrate that a basic computerized AIS without support from advanced analytical tools has no significant effect on fraud detection in isolation. This effectiveness only becomes apparent when the system is combined with more advanced technologies such as BDA and a strong, efficient internal audit unit.\u003c/p\u003e \u003cp\u003eThese research discrepancies emphasize that mere automation and mechanized data recording are insufficient; rather, the true value of AIS in high-risk banking environments depends on its integration with intelligent analytical capabilities and precise human/organizational oversight. Such an approach not only elevates the system from a simple recording tool to a dynamic platform for internal control and proactive detection but also helps reduce false positives and enhance the accuracy of identifying genuine anomalies. These findings indicate that pure automation and process mechanization alone are inadequate; instead, leveraging advanced analytical intelligence and rigorous human/organizational supervision are key factors in achieving true effectiveness in audit systems [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the benefits of AI in auditing are widely praised, a growing body of research adopts a cautious perspective, focusing on serious implementation challenges such as algorithmic bias, the lack of unified standards, transparency issues, and ethical risks. These studies demonstrate that the path to integrating AI into audit processes is far from straightforward and obstacle-free, requiring a systematic approach to technology governance and risk management.\u003c/p\u003e \u003cp\u003eUltimately, despite the growing enthusiasm for the concept of integrated auditing, its absolute and universal superiority over separated auditing remains questionable and may depend on specific institutional factors, cultural-legal differences, and measurement variables as evidenced by the diverse and sometimes conflicting findings in the existing literature. This variety of opinions underscores the need for further comparative research and greater attention to contextual factors in evaluating audit approaches.\u003c/p\u003e \u003cp\u003eIn conclusion, the integration of internal auditing into AIS frameworks for real-time monitoring of bank balances represents a dynamic, strategic, and vital domain for the transformation and advancement of the banking industry. The existing literature clearly points toward a future in which auditing will be fully continuous, data-driven, and intelligent leveraging the capabilities of AI, big data analytics, and predictive monitoring to proactively manage risk and ensure sustainable value creation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, fully realizing this vision requires overcoming existing challenges. Future research should focus on addressing the identified gaps, including the development of valid conceptual and empirical models tailored to specific contexts for real-time monitoring, the formulation of globally accepted standards for AI governance in banking auditing, and deeper examination of the practical, technical, and ethical challenges of this technological transformation (such as algorithmic bias, model transparency, data security, and accountability).\u003c/p\u003e \u003cp\u003ePrioritizing these areas will not only advance the scientific progress of the field but also provide the necessary foundation for the successful and responsible implementation of CA in the era of digital banking, ultimately contributing to enhanced sustainability, transparency, and trust in the global banking system [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research methodology","content":"\u003cp\u003eThis research is applied in purpose and descriptive-correlational in terms of data collection method, adopting a mixed-methods approach. The research design is based on a convergent parallel mixed-methods design, in which quantitative and qualitative data are collected and analyzed concurrently, with triangulation applied during the interpretation phase to integrate the results. This approach facilitates a more comprehensive and multidimensional understanding of the phenomenon under study and enhances the internal validity of the research through the mutual complementarity of quantitative results (with statistical precision) and qualitative findings (with explanatory depth).\u003c/p\u003e \u003cp\u003eThe study population comprises professionals employed in Iran's banking and financial sector (internal auditors, financial managers, information technology experts, and researchers in the finance/technology domain). Sampling for the quantitative component was conducted using a convenience random method, yielding 260 completed questionnaires. For the qualitative component, semi-structured interviews were carried out with 15 key experts (selected purposively based on criteria of expertise and practical experience in internal auditing and accounting information systems) to provide deeper insights.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.1 Analysis and Data Collection Tools\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eQuantitative component\u003c/b\u003e: The research was conducted using a survey method, with the primary data collection tool being a structured questionnaire designed based on the key research dimensions (AIS implementation and adoption, real-time monitoring of account balances, data accuracy and quality, system integration, auditor skills, information security, internal audit effectiveness, and data transparency). The questionnaire items were developed drawing on existing literature [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], employed a five-point Likert scale (ranging from \"strongly disagree\" to \"strongly agree\"). Following the collection of usable questionnaires, the Kolmogorov-Smirnov test was initially performed to assess the normality of the data distribution, the results of which confirmed non-normality. Consequently, non-parametric tests were utilized. Relationships among variables were examined using Spearman's correlation test. Additionally, the moderating role of variables such as auditor skills and process integration was evaluated through hierarchical regression. Statistical analyses were performed using SPSS software.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eQualitative component\u003c/b\u003e: Semi-structured interviews were conducted using an interview guide comprising open-ended questions on the benefits, challenges, and recommendations for implementing the integration of internal auditing into AIS. The interview data were analyzed using Braun and Clarke\u0026rsquo;s (2006) thematic analysis method, which involved stages of familiarization with the data, initial coding, theme extraction, review, and final theme definition. Over 830 meaningful statements were extracted and grouped into primary and secondary themes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntegration\u003c/b\u003e: Quantitative and qualitative results were triangulated during the interpretation phase to provide deeper explanations for both confirmed and rejected findings.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe content validity of the questionnaire was confirmed through expert judgment from 8 specialists in auditing and information systems (CVR exceeding 0.79 for most items). Construct validity was assessed via confirmatory factor analysis. Reliability was established by calculating Cronbach's alpha, yielding 0.87 for the overall questionnaire and values ranging from 0.78 to 0.91 for individual dimensions, indicating acceptable reliability. In the qualitative component, credibility was ensured through source triangulation and member checking.\u003c/p\u003e"},{"header":"4. Research Hypotheses","content":"\u003cp\u003e\u003cstrong\u003e4.1 Descriptive statistics of research variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic Information. The frequency and frequency percentage are as follows.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eRespondent\u0026apos;s side\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003eValid Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eCumulative Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eValid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eResearcher (Finance/Technology)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eFinancial Institutions / Banks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eAuditor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e78.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eInformation Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e89.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eChief Financial Officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eType of Company Activity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eValid Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCumulative Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eValid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eCommerce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eproductive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e33.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eService\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e76.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eTechnology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNumber of Employees\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003eValid Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eCumulative Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eValid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMore than 50 people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eBetween 10 and 50 people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e45.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e45.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eLess than 10 people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e47.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e47.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompany Activity History\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003eValid Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eCumulative Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eValid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMore than 10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eBetween 3 and 10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eLess than 3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Research Variables. The mean and standard deviation of the research variables are as follows.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eValid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eReal-time monitoring of bank account balances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eData accuracy and quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eIntegration of the internal audit system with other departments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eAuditors\u0026apos; Skills and Expertise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eSecurity and confidentiality of banking information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eEffectiveness and Value of Internal Audit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eConsequences and User Satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eTransparency and reliability of banking data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eThe method of data distribution was investigated using Kolmogorov-Smirnov test and the results showed that the data distribution was not normal, so non-parametric tests were used.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eTests of Normality\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eKolmogorov-Smirnova\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eShapiro-Wilk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eReal-time monitoring of bank account balances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eData accuracy and quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eIntegration of the internal audit system with other departments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eAuditors\u0026apos; Skills and Expertise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eSecurity and confidentiality of banking information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eEffectiveness and Value of Internal Audit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eConsequences and User Satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eTransparency and reliability of banking data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003ea. Lilliefors Significance Correction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMain Hypothesis:\u0026nbsp;\u003c/strong\u003eThe integration of internal auditing into AIS frameworks enhances the effectiveness and accuracy of real-time monitoring of bank balances.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eThe quantitative results of the study indicated that internal audit integration into AIS generally improves the effectiveness and accuracy of real-time monitoring, as certain subsidiary hypotheses\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003esuch as the positive relationship with reporting accuracy and the moderating roles of auditor skills and process integration\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ewere supported. However, others, including increased speed of error detection and improved transparency, were rejected.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eQualitative findings from the thematic analysis of interviewees\u0026apos; responses complement these results. For instance, the primary theme of \u0026quot;Positive Effects of Integration\u0026quot; included sub-themes such as \u0026quot;Real-time data analysis and faster decision-making\u0026quot; (14% frequency) and \u0026quot;Enhanced review efficiency and faster error detection\u0026quot; (14%), demonstrating improved monitoring effectiveness. In contrast, challenges such as \u0026quot;Weaknesses in software infrastructure and security concerns\u0026quot; (6%) explain why certain aspects, like transparency, were not supported in the quantitative results.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eFurthermore, qualitative recommendations\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003esuch as \u0026quot;Managerial support and development of unified standards\u0026quot; (7%)\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eemphasize that strengthening these factors could further enhance the overall effectiveness of the integration and improve the accuracy of real-time monitoring of bank balances.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Inferential statistics\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSub-hypothesis 1:\u003c/strong\u003e \u003cstrong\u003eThe integration of internal audit into the AIS has a significant positive relationship with the accuracy of real-time bank balance reports.\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate this hypothesis, Spearman\u0026apos;s correlation test was used. A significance value less than 0.05 indicates that there is a significant relationship between the integration of internal audit in AIS and the accuracy of real-time bank balance reports.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eCorrelations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003eData accuracy and quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSpearman\u0026apos;s rho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.153\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eData accuracy and quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.153\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eCorrelation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;results using Spearman\u0026apos;s correlation test showed that there was a positive and significant relationship between \u0026nbsp; the integration of internal audit into the AIS and the accuracy of real-time reports, which confirms this hypothesis. The qualitative findings complement this result with the theme \u0026quot;Positive effects of integration\u0026quot; and the sub-theme \u0026quot;Increasing transparency and reducing manual errors\u0026quot; (with a frequency of 11%), where interviewees emphasized \u0026quot;the use of integrated tools.\u0026quot; has increased transparency and reduced manual errors in reporting\u0026quot;, which directly refers to improving the accuracy of reports. Also, the theme \u0026quot;Synergy between units and improving information flow\u0026quot; (12%) suggests that integration leads to more accurate data, while challenges such as \u0026quot;lack of coordination between teams\u0026quot; (5%) may affect accuracy in certain cases, but reinforce the totality of quantitative findings.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSub-hypothesis 2:\u003c/strong\u003e \u003cstrong\u003eIntegrating internal audit into the AIS increases the speed of detection of banking errors and anomalies in real time.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate this hypothesis, Spearman\u0026apos;s correlation test was used. A significance value greater than 0.05 indicates that the integration of internal audit into AIS does not increase the speed of detection of banking errors and anomalies instantaneously.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eCorrelations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003eReal-time monitoring of bank account balances\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eSpearman\u0026apos;s rho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eReal-time monitoring of bank account balances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eQuantitative results from Spearman\u0026apos;s correlation indicated no significant relationship (r = 0.105, p = 0.092), leading to rejection of the hypothesis. This may be attributed to measurement limitations (e.g., perceptual scale sensitivity in a transitional context) or sample-specific factors such as legacy system prevalence in the studied banks. However, qualitative thematic analysis strongly supports the potential benefit, with the sub-theme \u0026quot;Increase the efficiency of checks and speed of error detection\u0026quot; (14% frequency) highlighting interviewees\u0026apos; views that \u0026quot;integration of audit and IT increased efficiency and speed of error detection.\u0026quot; This complementarity suggests that while current infrastructure constrains observable effects, targeted technological upgrades could realize these gains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSub-hypothesis 3:\u003c/strong\u003e \u003cstrong\u003eIntegrating internal audit into the AIS increases the transparency and reliability of real-time banking data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate this hypothesis, Spearman\u0026apos;s correlation test was used. A significance value greater than 0.05 indicates that the integration of internal audit into the AIS does not increase the transparency and reliability of banking data in real time.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eCorrelations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eTransparency and reliability of banking data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eSpearman\u0026apos;s rho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eTransparency and reliability of banking data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe quantitative results of Spearman\u0026apos;s correlation test indicate that there is no significant relationship (correlation coefficient 0.119, p=0.055) between internal audit integration into AIS and increased transparency and reliability, which refutes the hypothesis. Qualitative findings complement this result by providing more in-depth explanations, the theme of \u0026quot;positive effects of integration\u0026quot; and the subtheme of \u0026quot;enhancement\u0026quot; Transparency and Reducing Manual Errors\u0026quot; (11%) supports the potential of integration, with quotes such as \u0026quot;The use of integrated tools has increased transparency and reduced manual errors in reporting\u0026quot;, but challenges such as \u0026quot;security concerns\u0026quot; (6%) and \u0026quot;lack of coordination between teams\u0026quot; (5%) suggest why transparency has not increased in practice, and qualitative suggestions such as \u0026quot;Strengthening Data Security\u0026quot; (6%) suggest that by removing these barriers, The reliability of data in real-time can be improved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSub-hypothesis 4: The skills and expertise of internal auditors play a moderating role in the effect of internal audit integration on instantaneous banking supervision.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTo\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eevaluate this hypothesis, regression was used. The first table shows the value of the coefficient of determination, which explains the percentage of fit of the model. This value is equal to 16.3%.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 564px;\"\u003e\n \u003cp\u003e\u003cem\u003eModel Summary\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eR Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003eAdjusted R Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.163a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.381731048046937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 564px;\"\u003e\n \u003cp\u003ea. Predictors: (Constant), Moderator 4, Implementation and Adoption of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn the second table, the hypothesis is confirmed or rejected according to the significance value, given that the significance value is less than 0.05, so the hypothesis is confirmed.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eANOVAa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.031b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e37.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e38.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003ea. Dependent Variable: Real-time monitoring of bank account balances\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eb. Predictors: (Constant), Modulator4, Implementation and Adoption of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe following table shows the regression equation which is observed due to the significant value of the moderation role being greater than 0.05.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eCoefficientsa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e14.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eModulator4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003ea. Dependent Variable: Real-time monitoring of bank account balances\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHierarchical regression showed a significant overall model (R\u0026sup2; = 0.027, F = 3.529, p = 0.031), with a positive main effect of integration (\u0026beta; = 0.163, p = 0.009). However, the moderation term for auditors\u0026apos; skills was non-significant (\u0026beta; = 0.004, p = 0.945), indicating no moderating role in this sample. This aligns with heterogeneous skill distribution and external constraints (e.g., limited training access). Qualitative findings from the \u0026quot;Integration Challenges\u0026quot; theme (sub-theme \u0026quot;Employee Resistance and Lack of Technical Skills\u0026quot;, 5%) explain this: interviewees noted resistance and skill gaps as major barriers. The \u0026quot;Suggestions for Improvement\u0026quot; theme (sub-theme \u0026quot;Practical Training and Joint Workshops\u0026quot;, 6%) proposes solutions, suggesting the moderating role could emerge under improved conditions. Thus, the rejection appears context-dependent rather than absolute.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSub-hypothesis 5:\u003c/strong\u003e \u003cstrong\u003eIntegrating internal audit into AIS reduces the risks of fraud and misuse of bank balances in real time.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate this hypothesis, Spearman\u0026apos;s correlation test was used. A significance value greater than 0.05 indicates that the integration of internal audit into the AIS does not reduce the risks of fraud and misuse of bank balances in the moment.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eCorrelations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eSecurity and confidentiality of banking information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eSpearman\u0026apos;s rho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eSecurity and confidentiality of banking information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe results of the quantitative Spearman correlation test showed no significant relationship (correlation coefficient 0.076, p=0.221) between the integration of internal audit into AIS and the reduction of fraud risks, which rejects the hypothesis. Qualitative findings corroborate this result with the theme \u0026quot;Integration challenges\u0026quot; and the sub-theme \u0026quot;Software Infrastructure Weakness and Concern \u0026ldquo;Security\u0026quot; (6%) supplements, explaining why the risk mitigation was not observed, with quotes such as \u0026quot;weak software infrastructure and data security concerns have made full implementation difficult.\u0026quot; However, the theme of \u0026quot;Suggestions for Improvement\u0026quot; such as \u0026quot;Creating an Intermediate Team and Strengthening Data Security\u0026quot; (6%) suggests that by focusing on security, integration can reduce fraud risks in the future and offset quantitative findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSub-hypothesis 6:\u003c/strong\u003e \u003cstrong\u003eThe degree of integration of the organization\u0026apos;s financial and control processes determines the intensity of the impact of the integration of internal audit into the AIS on the real-time monitoring of bank balances.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegression was used to evaluate this hypothesis. The first table shows the value of the coefficient of determination, which explains the percentage of fit of the model. This is 25.9 %.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 546px;\"\u003e\n \u003cp\u003e\u003cem\u003eModel Summary\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eR Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003eAdjusted R Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.259a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.373727446845992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 546px;\"\u003e\n \u003cp\u003ea. Predictors: (Constant), Modulator6, Implementation and Adoption of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn the second table, the hypothesis is confirmed or rejected according to the significance value, given that the significance value is less than 0.05, so the hypothesis is confirmed.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eANOVAa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.000b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e35.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e38.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003ea. Dependent Variable: Real-time monitoring of bank account balances\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eb. Predictors: (Constant), Modulator6, Implementation and Adoption of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe following table shows the regression equation which is observed due to the significant value of the moderation role being greater than 0.05.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eCoefficients a\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e14.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eImplementation and Acceptance of Internal Audit in AIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eModulator6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReal-time monitoring of bank account balances = 3.203 + 0.118 \u0026times; Implementation and adoption of internal audit in AIS + 0.063 \u0026times; Degree of integration of financial and control processes in the organization\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ea. Dependent Variable: Real-time monitoring of bank account balances\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe degree of integration of the organization\u0026apos;s financial and control processes determines the intensity of the impact of internal audit integration in the AIS on the real-time monitoring of bank balances. The quantitative results of regression indicate the moderating role of integrity (R\u0026sup2;=0.067, p=0.000 for the overall model, p=0.001 for the modifier), which confirms the hypothesis. The qualitative findings corroborate this result with the theme \u0026quot;Positive effects of integration\u0026quot; and the sub-theme \u0026quot;Synergy between units and flow improvement.\u0026quot; Information\u0026quot; (12%) completes, where interviewees stated that \u0026quot;this merger has led to synergy between finance and technology units and improved information flow\u0026quot;, which directly points to the severity of the integration impact. Also, the challenge of \u0026quot;lack of coordination between teams and lack of integrated standards\u0026quot; (5%) explains how a lack of integration can reduce the impact of integration, and the proposal for \u0026quot;developing integrated tools and pilot projects\u0026quot; (6%) offers a solution to reinforce this moderating role.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Thematic Analysis Interview Responses:\u0026nbsp;\u003c/strong\u003eThe responses of the interviewees were analyzed using Brown and Clark (2006) thematic analysis method. After open coding, more than 830 meaningful sentences/phrases were extracted and grouped into main themes. The main themes are divided into 4 categories:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinal Table of Main and Sub-Themes\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"right\"\u003e\n \u003ctable dir=\"rtl\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eMain Theme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003esubstrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eApproximate frequency (of total phrases)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eDirect Quote Example\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003ePositive Effects of Integration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eReal-time data analysis and faster decision-making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e119 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;Integrating processes has enabled real-time data analysis and faster management decision-making.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eIncrease the efficiency of checks and speed of error detection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e117 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;The integration of audit and information technology has increased the efficiency of investigations and increased the speed of error detection.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eSynergy between units and improved information flow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e101 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;This integration has led to synergies between the financial and technology units and improved the flow of information.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eIncreasing transparency and reducing manual errors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e93 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;The use of integrated tools has increased transparency and reduced manual errors in reporting.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eIntegration Challenges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eWeak software infrastructure and security concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e53 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;Weak software infrastructure and data security concerns have made full implementation difficult.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eImplementation costs and the need for specialized training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e49 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;Implementation and maintenance costs, as well as the need for specialized training, are a barrier for small businesses.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eLack of coordination between teams and lack of integrated standards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e45 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;The lack of coordination between audit and IT \u0026nbsp; \u0026nbsp; teams and the lack of unified standards are challenging.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eEmployee Resistance and Lack of Technical Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e43 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;Staff resistance to change and lack of technical skills are among the most important obstacles.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eSuggestions for improvement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eManagement support and development of integrated standards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e56 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;He emphasizes that managerial support and the development of integrated standards are essential for success.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eHands-on training and joint workshops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e51 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;It is recommended that practical training and joint workshops be held between audit and IT.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eBuild an interdepartmental team and strengthen data security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e51 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;Creating an interdepartmental team and strengthening data security is an effective solution.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eDevelopment of integrated tools and pilot projects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e47 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;It proposes the development of integrated tools and the launch of pilot projects prior to implementation at the organizational level.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eInteraction between departments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp dir=\"LTR\"\u003eAutomatic synchronization and increased process efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026quot;With the implementation of this integration, there will be an automatic synchronization between different departments of the organization, and the system will increase the efficiency of accounting and banking processes.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Complementing the results of hypotheses with qualitative findings\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe mixed-methods approach employed in this study, by enabling triangulation between quantitative and qualitative findings, led to a deeper and more multifaceted understanding of the phenomenon of integrating internal auditing into AIS frameworks.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eThe quantitative results provided partial support for the main hypothesis, demonstrating a positive and significant relationship between internal audit integration and financial reporting accuracy, as well as confirming the moderating roles of auditor expertise and the degree of organizational process integration. However, no significant relationships were found for variables such as the speed of anomaly detection, data transparency, or direct reduction in fraud risk.\u003c/p\u003e\n\u003cp\u003eQualitative findings, derived from thematic analysis of semi-structured interviews, served to explain and enrich the quantitative results. The primary theme of \u0026quot;Positive Effects of Integration\u0026quot;\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eencompassing sub-themes such as real-time data analysis, enhanced efficiency of review processes, inter-unit synergy, and reduction of manual errors\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ehighlighted the substantial potential of this integration within\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eAIS frameworks. These findings indicate that in organizations with adequate technological infrastructure and sufficient managerial support, outcomes such as faster decision-making and improved information flow have been tangibly realized. These qualitative insights effectively account for why certain quantitatively supported hypotheses\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eincluding improved reporting accuracy and the moderating role of process integration\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ewere also observable in practice.\u003c/p\u003e\n\u003cp\u003eConversely, the theme of \u0026quot;Integration Challenges\u0026quot;\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eincluding issues such as weaknesses in software infrastructure, security concerns, employee resistance to change, shortages of specialized technical skills, and lack of effective inter-team coordination\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eclearly elucidates the reasons for the non-confirmation of certain subsidiary hypotheses. For example, despite the theoretical potential of integration to increase anomaly detection speed and reduce fraud risk, practical limitations\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003esuch as dependence on legacy systems and skills gaps\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ehindered the full realization of these benefits in the banks studied.\u003c/p\u003e\n\u003cp\u003eFurthermore, the theme of \u0026quot;Recommendations for Improvement\u0026quot; emphasizes the necessity of practical and ongoing training, strengthened data security mechanisms, increased managerial support, and the development of integrated platforms\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eactions that could address the shortcomings observed in the quantitative results in future research and implementations, thereby enabling more efficient exploitation of the integration\u0026apos;s benefits.\u003c/p\u003e\n\u003cp\u003eUltimately, the triangulation of findings reveals that the integration of internal auditing into AIS is, from a conceptual and potential standpoint, a highly effective approach. However, fully realizing its benefits is substantially dependent on overcoming organizational, technical, and human barriers. This convergence and mutual complementarity of quantitative and qualitative results not only strengthened the internal validity of the study but also\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ecompared to single-method studies\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003edelivered more practical and realistic insights for policymakers, managers, and researchers in the fields of auditing and information systems.\u003c/p\u003e"},{"header":"5. Discussion and Analysis","content":"\u003cp\u003eThe findings of this study provide partial empirical support for the integration of internal audit functions into Accounting Information Systems (AIS) frameworks as a mechanism to enhance real-time monitoring of bank balances. Consistent with agency theory, the positive relationship between AIS-internal audit integration and financial reporting accuracy (sub-hypothesis 1) confirms that such integration can reduce information asymmetry and improve data reliability a result aligned with prior studies in banking contexts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Similarly, the significant moderating role of organizational process integration (sub-hypothesis 6) underscores the importance of cohesive financial and control processes in amplifying the benefits of integration, echoing the knowledge spillover effect observed in integrated auditing literature (e.g., integrated auditing superiority in Chinese listed firms) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the non-significant results for sub-hypotheses 2 (anomaly detection speed), 3 (data transparency), and 5 (fraud risk reduction) indicate that the anticipated benefits do not always materialize uniformly in practice. These findings are consistent with documented implementation challenges in the literature, including legacy system constraints, data volume overload, and insufficient technological maturity (Ghadeer, 2022; Proceedings of the 8th International Conference on Big Data and Computing, 2023). The low R\u0026sup2; values in the regression models (0.027 for auditor skills moderation and 0.067 for process integration moderation) further suggest that a substantial portion of variance in real-time monitoring effectiveness remains unexplained, likely due to unmeasured contextual factors such as regulatory environment, organizational culture, and stage of digital transformation in the sampled Iranian banks.\u003c/p\u003e \u003cp\u003eThe convergent mixed-methods design proved particularly valuable in interpreting these discrepancies. While quantitative analyses constrained by perceptual scales and non-normal data distributions showed limited or no significance for several outcomes, the thematic analysis of expert interviews revealed substantial perceived potential. High-frequency positive sub-themes (e.g., \u0026ldquo;real-time data analysis and faster decision-making\u0026rdquo; at 14%, \u0026ldquo;increased efficiency of checks\u0026rdquo; at 14%, \u0026ldquo;synergy between units\u0026rdquo; at 12%) indicate that practitioners strongly believe in the transformative capacity of integration when infrastructural and human barriers are addressed. Conversely, prominent challenge themes (e.g., \u0026ldquo;weak software infrastructure and security concerns\u0026rdquo; at 6%, \u0026ldquo;employee resistance and lack of technical skills\u0026rdquo; at 5%) provide a plausible explanation for the non-significant quantitative results: current organizational and technological conditions in the studied context appear to suppress observable effects.\u003c/p\u003e \u003cp\u003eFrom a theoretical perspective, these results enrich agency theory by illustrating that effective internal audit integration can mitigate agency costs through enhanced real-time oversight, but only when supported by adequate structural alignment (process integration). The non-significant moderating role of auditors\u0026rsquo; skills (sub-hypothesis 4) suggests that, in transitional settings such as emerging markets, individual expertise alone may be insufficient without systemic training and cultural change a finding that aligns with calls for new competencies in AI-augmented auditing (Kuna, 2025; Olorunyomi et al., 2022).[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePractically, the study highlights actionable implications for banking institutions in resource-constrained environments: prioritizing investments in interoperable AIS platforms, cross-functional training programs, and pilot implementations could unlock the latent benefits identified qualitatively. For regulators and standard-setters, the results reinforce the need for updated governance frameworks addressing algorithmic transparency, bias mitigation, and ethical AI use in continuous auditing [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Research Limitations\u003c/h2\u003e \u003cp\u003eDespite efforts to provide a comprehensive perspective on the application of privacy-preserving technologies in AIS within the Iranian banking sector, this research encountered several methodological and contextual limitations that partially affect the generalizability and depth of its findings. First, the convenience sampling approach and focus on Iranian banking professionals limit generalizability to other contexts or more mature digital banking ecosystems. Second, reliance on self-reported perceptual data may introduce common method bias or social desirability effects, particularly regarding sensitive topics such as fraud detection and security. Third, the relatively low explained variance (R\u0026sup2; \u0026lt; 0.07 in moderation models) indicates that important predictors (e.g., specific technology adoption stage, organizational size effects, or external regulatory pressure) were not captured. Finally, while triangulation strengthened interpretive depth, the modest qualitative sample size (n\u0026thinsp;=\u0026thinsp;15) may not fully represent the diversity of perspectives across Iran\u0026rsquo;s heterogeneous banking sector.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSampling was conducted using a non-probability method, with the study population primarily limited to professionals employed in banks, financial institutions, the information technology sector in Iran. Although this approach facilitated access to relevant experts, it increases the risk of selection bias and necessitates caution in generalizing the results to the entire population of banking professionals in the country or to other developing nations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eReliance on self-reported data through questionnaires and semi-structured interviews introduces the potential for social desirability bias, particularly in a sensitive topic such as data privacy and security, where respondents may tend to align their views with socially or professionally desirable responses.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe research focused on experts' perceptions and opinions, lacking an examination of the actual implementation of privacy-preserving techniques (such as homomorphic encryption or federated learning) in operational environments of Iranian banks. This limitation stems from difficulties in accessing real banking systems due to organizational security and confidentiality considerations, resulting in findings that are more descriptive and perceptual than empirical and operational.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe absence of a comprehensive and transparent legal framework in Iran for personal data protection (comparable to GDPR in Europe), coupled with restrictions on access to sensitive banking information, constrained the potential for deeper empirical studies or analysis of real transactional data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe qualitative sample size (25 interviews), although proceeding until theoretical saturation was achieved, was limited relative to the broad diversity of financial organizations in Iran, potentially leaving some marginal or regional perspectives uncovered.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Directions for Future Research\u003c/h2\u003e \u003cp\u003eFuture studies could employ longitudinal designs to track the evolution of integration benefits over time, incorporate objective performance metrics (e.g., actual anomaly detection rates or fraud incident logs), or apply advanced robust statistical techniques (e.g., bootstrapped regression) to address non-normality. Comparative cross-country research, particularly between emerging and developed markets, would further clarify contextual moderators. Additionally, experimental or simulation-based studies examining AI governance mechanisms in real-time auditing could provide deeper insights into overcoming current barriers.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis research was conducted at a sensitive and pivotal time, when the banking sector is confronting rapid digital transformation, escalating cyber threats and risks, and increasingly stringent regulatory requirements. The findings demonstrate that the integration of internal auditing into AIS frameworks holds substantial potential for enhancing real-time monitoring of bank balances, thereby improving reporting accuracy, enabling proactive risk management, and strengthening stakeholder confidence.\u003c/p\u003e \u003cp\u003eDespite the identified practical challenges including weaknesses in infrastructure and skills gaps that currently hinder the full realization of these benefits triangulation of quantitative and qualitative results indicates that these barriers can be overcome through targeted investments in technology, training, governance frameworks. This study not only fills existing gaps in the scholarly literature and contributes to advancing the theoretical understanding of CA in digital banking but also provides valuable practical insights for banking professionals, internal auditors, information systems designers, and regulatory authorities.\u003c/p\u003e \u003cp\u003eUltimately, the successful integration of internal auditing into AIS transcends mere technological advancement; it emerges as a strategic imperative for sustaining the resilience and efficiency of the banking sector in an era of instantaneous transactions and complex risks. Implementing the recommendations proposed in this research can significantly bolster risk management, enhance operational efficiency, and preserve public trust in the banking system, while paving the way for the transition to the next generation of digital corporate governance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eParticipant Consent Statement\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants prior to their inclusion in the study. In cases where participants were unable to provide consent, consent was obtained from their legal guardians. The study protocol and consent procedures were reviewed and approved by the relevant ethics committee. Where applicable, the requirement for written informed consent was waived by the approving ethics committee in accordance with institutional and national regulations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlassuli (2024) Internal audit as a moderator of the relationship between accounting information systems and performance in Jordanian commercial banks. Banks Bank Syst 19(2):88\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp:doi.org/10.21511/bbs.19(2).2024.07\u003c/span\u003e\u003cspan address=\"http:10.21511/bbs.19(2).2024.07\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePermana \u0026amp; Kusumawati (2025) The Influence of Accounting Information Systems on Internal Audit Effectiveness in Hasanuddin University. 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Int J Frontline Res Multidisciplinary Stud 1(2):94\u0026ndash;112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.56355/ijfrms.2022.1.2.0057\u003c/span\u003e\u003cspan address=\"10.56355/ijfrms.2022.1.2.0057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eThe Influence of Accounting Information Systems on Internal Audit Effectiveness in Hasanuddin University Nalendra Bhayu Permana and Andi Kusumawati Hasanuddin University, Makassar, Indonesia\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2991/978-94-6463-758-8_198\u003c/span\u003e\u003cspan address=\"10.2991/978-94-6463-758-8_198\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Islamic Azad University, Tehran","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Internal Audit, Accounting Information Systems, Real-time Monitoring, Bank Balance, Continuous Auditing","lastPublishedDoi":"10.21203/rs.3.rs-9709682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9709682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the integration of internal audit functions into Accounting Information Systems frameworks to enable continuous, real-time monitoring of bank balances. Leveraging technologies such as artificial intelligence, big data analytics, and cloud computing, this integration aims to facilitate early anomaly detection, fraud risk reduction, and enhanced regulatory compliance. A convergent mixed-methods approach was employed: questionnaire data (n\u0026thinsp;=\u0026thinsp;260) from Iranian banking professionals were analyzed using non-parametric tests (Spearman's correlation) and hierarchical regression, complemented by thematic analysis of semi-structured interviews with 15 experts. Quantitative findings provide partial support for the main hypothesis, confirming a positive relationship between AIS-internal audit integration and financial reporting accuracy, as well as a significant moderating role of organizational process integration. However, no significant effects were found for anomaly detection speed, data transparency, fraud risk reduction, or the moderating role of auditors' skills (despite a significant overall model in some regressions). Qualitative insights reveal substantial potential benefits (e.g., real-time analysis, inter-unit synergy) alongside key barriers (e.g., infrastructure weaknesses, skills gaps, security concerns). Triangulation highlights that observed quantitative non-significances may stem from contextual limitations in emerging markets, while qualitative data suggest these benefits could materialize with targeted improvements (e.g., training, standards development). The study advances theoretical understanding of continuous auditing in digital banking and offers practical implications for risk management and AI governance in resource-constrained settings.\u003c/p\u003e","manuscriptTitle":"Real-Time Integration of Internal Auditing into AIS Frameworks for Monitoring Bank Balances","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 09:58:43","doi":"10.21203/rs.3.rs-9709682/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd622de4-d7bd-4480-af18-c51925de89eb","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":68120044,"name":"Finance"}],"tags":[],"updatedAt":"2026-05-18T09:58:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 09:58:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9709682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9709682","identity":"rs-9709682","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}