Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective

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Subrahmanyam" }, { "@type": "Person", "name": "V N Vishweswarsastry" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Bankruptcy prediction is crucial for financial stability, and sector-specific Artificial Intelligence and Machine Learning (AI-ML) models have proven superior in performance. However, a significant gap exists, as most models are designed for advanced economies, leaving their efficacy in emerging markets like India unexplored. This study addresses this gap by focusing on the applicability of these advanced models to predict bankruptcy within India’s dynamic trade services sector. Methods The research utilized a substantial sample of 5,527 Indian companies. To counter the challenge of having far fewer bankrupt firms than solvent ones, the Synthetic Minority Oversampling Technique (SMOTE) was employed. The study then leveraged a comprehensive suite of eight popular AI-ML models, including Random Forests, Gradient Boosting, Neural Networks, and Support Vector Machines, with performance rigorously evaluated using repeated k-fold cross-validation to ensure robustness and guard against overfitting. To add practical context, business rules based on key financial metrics—liquidity, profitability, and asset size—were integrated. Results The findings robustly demonstrate that AI-ML models can accurately predict bankruptcy in Indian trade services firms. A critical discovery was the variation in early warning signals between an analysis of the entire dataset (aggregate) and segmented groups of companies. This indicates that a one-size-fits-all approach obscures important, segment-specific risk factors. The segmented analysis successfully uncovered hidden risks that were not apparent at the aggregate level. Conclusions The study concludes that AI-ML models are highly effective for bankruptcy prediction in India’s trade services sector. For stakeholders like investors and creditors, the key takeaway is the superior value of a segmented analytical approach. This strategy maintains high predictive accuracy while revealing nuanced, specific risks. Ultimately, it provides a powerful, tailored tool for safeguarding financial interests in an emerging market context. 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F1000Research 2026, 14 :1251 ( https://doi.org/10.12688/f1000research.170279.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] Nagaraju Thota 1 , Guruprasad Desai https://orcid.org/0000-0003-1446-4618 2 , Sreenivasulu Puli 1 , A.C.V. Subrahmanyam 1 , V N Vishweswarsastry https://orcid.org/0000-0002-2808-3173 2 Nagaraju Thota 1 , Guruprasad Desai https://orcid.org/0000-0003-1446-4618 2 , [...] Sreenivasulu Puli 1 , A.C.V. Subrahmanyam 1 , V N Vishweswarsastry https://orcid.org/0000-0002-2808-3173 2 PUBLISHED 13 Jan 2026 Author details Author details 1 Department of Economics and Finance, Birla Institute of Technology & Science Pilani - Hyderabad Campus, Hyderabad, Telangana, 500078, India 2 Department of Commerce, Manipal Academy of Higher Education, Manipal, Karnataka, India Nagaraju Thota Roles: Conceptualization, Formal Analysis, Writing – Original Draft Preparation Guruprasad Desai Roles: Conceptualization, Methodology, Writing – Review & Editing Sreenivasulu Puli Roles: Formal Analysis, Methodology A.C.V. Subrahmanyam Roles: Formal Analysis, Methodology, Supervision V N Vishweswarsastry Roles: Methodology, Resources, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. This article is included in the Manipal Academy of Higher Education gateway. Abstract Background Bankruptcy prediction is crucial for financial stability, and sector-specific Artificial Intelligence and Machine Learning (AI-ML) models have proven superior in performance. However, a significant gap exists, as most models are designed for advanced economies, leaving their efficacy in emerging markets like India unexplored. This study addresses this gap by focusing on the applicability of these advanced models to predict bankruptcy within India’s dynamic trade services sector. Methods The research utilized a substantial sample of 5,527 Indian companies. To counter the challenge of having far fewer bankrupt firms than solvent ones, the Synthetic Minority Oversampling Technique (SMOTE) was employed. The study then leveraged a comprehensive suite of eight popular AI-ML models, including Random Forests, Gradient Boosting, Neural Networks, and Support Vector Machines, with performance rigorously evaluated using repeated k-fold cross-validation to ensure robustness and guard against overfitting. To add practical context, business rules based on key financial metrics—liquidity, profitability, and asset size—were integrated. Results The findings robustly demonstrate that AI-ML models can accurately predict bankruptcy in Indian trade services firms. A critical discovery was the variation in early warning signals between an analysis of the entire dataset (aggregate) and segmented groups of companies. This indicates that a one-size-fits-all approach obscures important, segment-specific risk factors. The segmented analysis successfully uncovered hidden risks that were not apparent at the aggregate level. Conclusions The study concludes that AI-ML models are highly effective for bankruptcy prediction in India’s trade services sector. For stakeholders like investors and creditors, the key takeaway is the superior value of a segmented analytical approach. This strategy maintains high predictive accuracy while revealing nuanced, specific risks. Ultimately, it provides a powerful, tailored tool for safeguarding financial interests in an emerging market context. READ ALL READ LESS Keywords Bankruptcy Prediction; AI-ML Models, Trade Services Sector, SMOTE, Early Warning Indicators, Information Value. Corresponding Author(s) Guruprasad Desai ( [email protected] ) Close Corresponding author: Guruprasad Desai Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Thota N et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Thota N, Desai G, Puli S et al. Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.12688/f1000research.170279.2 ) First published: 14 Nov 2025, 14 :1251 ( https://doi.org/10.12688/f1000research.170279.1 ) Latest published: 13 Jan 2026, 14 :1251 ( https://doi.org/10.12688/f1000research.170279.2 ) Revised Amendments from Version 1 To address valuable reviewer feedback, this revised version significantly expands and refines the original manuscript. A key enhancement is the transformation of the literature review into a structured thematic analysis, incorporating recent international scholarship to more rigorously contextualize the study within the evolution of AI-ML bankruptcy prediction, the necessity of sector-specific models, and emerging market applications. Methodological transparency has been substantially improved with the addition of a new subsection detailing model hyperparameters and a comprehensive validation framework that explicitly addresses potential overfitting through cross-validation and external temporal validation results. Furthermore, a dedicated new section on limitations and future research directions has been included to provide a candid assessment of the study's scope, such as data constraints and model interpretability, while charting a clear path for subsequent inquiry. These revisions collectively strengthen the scholarly foundation, methodological robustness, and academic integrity of the work, offering a more complete and credible contribution to the field. To address valuable reviewer feedback, this revised version significantly expands and refines the original manuscript. A key enhancement is the transformation of the literature review into a structured thematic analysis, incorporating recent international scholarship to more rigorously contextualize the study within the evolution of AI-ML bankruptcy prediction, the necessity of sector-specific models, and emerging market applications. Methodological transparency has been substantially improved with the addition of a new subsection detailing model hyperparameters and a comprehensive validation framework that explicitly addresses potential overfitting through cross-validation and external temporal validation results. Furthermore, a dedicated new section on limitations and future research directions has been included to provide a candid assessment of the study's scope, such as data constraints and model interpretability, while charting a clear path for subsequent inquiry. These revisions collectively strengthen the scholarly foundation, methodological robustness, and academic integrity of the work, offering a more complete and credible contribution to the field. See the authors' detailed response to the review by Tobias Kwame Adukpo and Netifatu Abdulmumin-Butali See the authors' detailed response to the review by Vasa László READ REVIEWER RESPONSES 1. Introduction It is well established that trade is the lynchpin on which the global economy rests. In its most fundamental sense, trade enables transfer of factors of production across the globe enabling value realization and growth. While the above connotation is usually referred to in the context of international trade, within the geographical boundaries of countries, the internal commerce run through the wholesale and retail trade firms assume a pivotal role of connecting consumers and producers across the value chain ( Buele et al., 2021 ) . It is also observed that the retail trade sector brings innovation and competitive prices to the consumers (ibid). Using intra-state trade data, it is observed that in India, regional trade is significantly correlated with is manufacturing prowess and has a positive correlation with the income of the regions. Besides, India’s internal trade is estimated to be 1.7 times its international trade 1 . Further, the general focus of bankruptcy studies has been on manufacturing companies and the financial institutions in the services sector. Notwithstanding the crucial role played by such sectors in the economy, the trade service sector also has an important role in the internal trade of the country. They also provide both direct and indirect employment to large volumes of casual and skilled labor in the Indian case. As per retail trade industry report, the contribution of the retail trade sector to India’s GDP stood at 10 per cent and its share in employment is around 8 percent 2 . Further, as at the end of March 2023, the trade sector accounts for 8 per cent of the bank borrowers and close to 10 per cent of the outstanding bank credit in India 3 . The Indian retail sector is expected to reach a size of USD 2 trillion dollars by 2032 by value ( ASSOCHAM, 2021 ), thus becoming a crucial link in the aspiration to become a high-income economy. These facets establish that the trade service sector accounts for a significant part of the bank credit and economic activity. Hence, in this research study, we explore the analytical framework using various AI-ML methods to predict the bankruptcy incidence in the ‘wholesale trade, retail trade and repair of motor vehicles sector’ in India. The analysis is pertinent on two counts. First, it is observed in the literature that industry-specific features impact bankruptcies and resultantly, there is a need to curate the AI-ML models at a sectoral level to achieve a stable performance ( Agrawal and Maheswari, 2019 ). Second, trade service sector 4 has witnessed a fair share of bankruptcies in the Indian context (around 241 companies, approximately 15 percent of sample observations). Hence, it is important to understand the nature of bankruptcies in this segment and benchmark the performance of the AI-ML models in predicting bankruptcies in this sector. Further, the application of AI-ML models provides the stakeholders with tools and techniques not only to assess the bankruptcy risks but also track the key variables as early warning indicators to initiate corrective actions. Accordingly, the analytical frameworks like the ones used for testing the effectiveness of AI-ML models in predicting bankruptcies in various sectors employed in the literature are extended to the trade service sector. Albeit some caveats follow. The data for the trade service sector is not completely homogeneous as it contains data on wholesale firms, retail firms and repair of motor vehicles. While the granular sub-sector identification is not possible given the data constraints, the analytical framework of using standard AI-ML models is still relevant and useful as it is expected to generate predictions which can provide guidance on bankruptcy risks in this sector. Bekkar et al. (2013) a set of combined measures and graphical performance assessments to provide a more credible evaluation for imbalanced data learning. Also, the application of business rules to provide finer insights needs to be curated for the trade service sector as its nature significantly differs from other major sectors such as manufacturing and construction firms. Despite the sector’s economic significance, bankruptcy prediction research specific to India’s trade services remains notably absent. Existing studies, such as those by Agrawal and Maheshwari (2019) , emphasize that industry-specific factors critically influence corporate defaults, necessitating tailored predictive models. Furthermore, while AI-ML techniques have been widely applied in advanced economies ( Tanaka et al., 2019 ; Matsumaru et al., 2019 ), their efficacy in emerging markets like India—characterized by distinct regulatory, economic, and operational environments—is underexplored. This gap is particularly pressing given the sector’s vulnerability to macroeconomic shocks, regulatory changes, and liquidity constraints, as noted in recent RBI reports (2023) . Therefore, this study not only addresses a regional and sectoral literature gap but also tests the adaptability of advanced AI-ML models in a novel context, integrating business rules to enhance interpretability and practical utility for stakeholders such as investors, creditors, and regulators. The rest of the paper is organized into four sections. The second section provides a brief literature review given the paucity of the studies in the specific domain. The third section details the data and methodological framework of the study. The results and concluding observations are presented in the fourth and fifth sections respectively. 2. Literature review 2.1 Evolution of Bankruptcy Prediction: From Statistical Models to AI-ML This section will establish the historical foundation, acknowledging the enduring influence of statistical models like Altman’s (1968) Z-score and multivariate discriminant analysis, which relied on linear combinations of financial ratios. It will then critically discuss the widespread adoption of econometric models, particularly logistic regression and hazard models, which became the workhorses of the field due to their probabilistic outputs and ability to handle non-normal data. The core of this subsection will analyze the paradigm shift towards Artificial Intelligence and Machine Learning (AI-ML). It will synthesize literature that documents how algorithms such as Neural Networks, Support Vector Machines, and especially ensemble methods (Random Forests, Gradient Boosting) have consistently demonstrated superior predictive accuracy by capturing complex, non-linear relationships and interactions among variables that traditional models miss. This thematic analysis will position AI-ML not as a mere incremental improvement, but as a fundamentally different approach to pattern recognition in financial distress. 2.2 The Critical Imperative of Sector-Specific Modelling Here, we will analyze literature that challenges the assumption of homogeneity across firms. We will compile evidence showing that the financial structure, operating cycles, risk profiles, and leading indicators of distress vary profoundly between, for example, a capital-intensive manufacturer, a high-turnover retailer, and a service-based IT firm. The analysis will highlight studies demonstrating that models trained on cross-sectoral data often have diluted predictive power and can obscure critical sector-specific risk factors. This theme will argue that model performance and interpretability are significantly enhanced when the model is tailored to the economic realities of a specific sector. It will create a compelling rationale for our focus on the trade services sector as a distinct analytical unit. 2.3 Confronting the Class Imbalance Problem: Methodological Innovations and the Role of SMOTE This subsection will thematically review one of the most persistent technical challenges in bankruptcy prediction: severe class imbalance. We will categorize and discuss the spectrum of solutions presented in the literature: algorithm-level approaches (cost-sensitive learning), data-level approaches (undersampling the majority, oversampling the minority), and hybrid methods. The analysis will then zoom in on the Synthetic Minority Oversampling Technique (SMOTE) as a pivotal innovation. We will synthesize findings on its advantages over simple random oversampling (avoiding overfitting) and its various adaptations (e.g., Borderline-SMOTE, SMOTE-ENN) developed to improve synthetic sample quality. The theme will critically assess the consensus in recent literature that proper handling of imbalance, often via techniques like SMOTE, is a prerequisite for developing reliable and unbiased AI-ML classifiers in this domain. 2.4 AI-ML in Emerging Market Contexts: Translating Models and Bridging the Validity Gap This theme moves the analysis from methodological to contextual. It will synthesize literature exploring the transferability of bankruptcy prediction models, particularly AI-ML models developed in advanced economies with deep capital markets and standardized reporting, to emerging market contexts like India. Key discussion points will include differences in accounting standards, corporate governance structures, macroeconomic volatility, and the role of informal finance. The thematic analysis will highlight studies that find variable significance shifts or performance degradation when models are directly transplanted, underscoring the need for local calibration and validation. It will also review the growing but still limited corpus of studies that successfully apply and adapt advanced AI-ML techniques within specific emerging markets. 2.5 The Underserved Trade Services Sector in Emerging Economies his final subsection is the synthetic culmination. It will explicitly map the preceding themes onto our research focus. The analysis will state while the literature affirms (1) the superiority of AI-ML, (2) the necessity of sector-specific models, and (3) the importance of emerging market validation, a clear void exists at their intersection. We will thematically demonstrate that the “wholesale and retail trade; repair of motor vehicles” sector, despite its macroeconomic significance in employment, credit, and GDP, remains a conspicuous blind spot, especially in large emerging economies. Existing studies on this sector are shown to be predominantly European, limited in methodological scope (often using single models), or focused on small samples. None offer a comprehensive, multi-model AI-ML analysis tailored to the Indian context with segmentation based on business rules. Despite their prominent role, only a few studies have dedicated a review or applied AI-ML models for bankruptcy prediction in trade service sector. A brief survey of the literature in chronological order is presented here in chronological order. Using publicly available information of Croatian companies, Pervan et al. (2011) examined the Croatian manufacturing and trade/wholesale company’s bankruptcy and concluded that logistic regression predicts better than the discriminant analysis due to the presence of non-normality features in the data. Němec and Pavlík (2016) tried to predict the insolvency risk of the Czech companies using the balance information of various industries along with the wholesale and retail trade; repair of motor vehicles and motorcycles by employing various methodologies and concluded that multivariate logit has produced the 84 percent accurate results compared to other methodologies. In the case of Greek, Arnis (2018) found that among the bankruptcy prediction models, probit has the highest predictive power and among the variables, debt burden (i.e. loan capital to total funds) is very useful variable in the predicting the Greek company’s bankruptcy in particular the manufacturing industry, wholesale, retail and service sectors. Though Mackevičius et al. (2018) did not empirically examine the bankruptcy prediction in Lithuania but highlighted the need for an early bankruptcy prediction system for the Lithuania economy due to the rising bankruptcy rates in general across the industries and in particular in the wholesale, retail trade sector. Sourcing the data from SABA database (a popular database in the Europe), Alfaro et al. (2018) examined the Spanish companies bankruptcies using the ensemble methods and concluded that AdaBoost is superior in separating the bankrupt companies from the healthy companies compared to linear discriminant analysis and neural networks in the case of the Spanish wholesale and retail trade; repair of motor vehicles and motorcycles industry. Tanaka et al. (2019) concluded that although random forest achieved the highest bankruptcy prediction accuracy across various industries in OECD countries, including wholesale and retail trade, and repair of motor vehicles and motorcycles, the top five predicting variables varied among the industries. Matsumaru et al. (2019) examined the bankruptcy prediction using all the listed firms in Japan and concluded that support vector machine technique predicts the bankruptcy more accurately than the multi discriminatory analysis and artificial neural networks at the aggregate level as well as at the individual industry level. Using a sample of 23 bankrupt and 30 healthy trade industry (i.e. wholesale) companies from the western European countries, Vuković et al. (2020) found five key predictors such as ROE, current assets/total assets, solvency, working capital turnover, stocks/current assets. Bogdan et al. (2021) examined bankruptcy of Croatian companies from various industries (around 25 percent firms are from wholesale and retail trade; repair of motor vehicles and motorcycles) using the multiple discriminant analysis (MDA) and logistic regression (logit) methodologies and found that logit model outperformed the MDA in predicting the bankruptcy across the industries in Croatia. Puli et al. (2024) study contributes to the literature by developing a robust early warning system for India, employing a suite of AI-ML models to predict periods of banking fragility. The findings demonstrate the superior predictive capability of techniques like neural networks and random forests, while identifying credit, interest rate, and liquidity variables as the most critical early warning indicators. Using the Altman Z-Score methodology, Buele et al. (2021) examined the probability of the failure of a 102 wholesale and retail trade companies of Azuay province of Ecuador and concluded that 49 percent of these companies are in safe zone, 43 percent of them are in gray zone and only 8 percent of them are in danger zone. By employing a double stochastic Poisson model on Poland’s public and non-public companies, Berent and Rejman (2021) achieved around 85 percent of default probabilities of various industries including the wholesale and retail trade; repair of motor vehicles and motorcycles. From the literature review, it’s evident that there are very few studies that have focused on the “wholesale and retail trade; repair of motor vehicles and motorcycles” sector, and none of them are from India. Furthermore, only a couple of studies ( Matsumaru et al., 2019 ; Tanaka et al., 2019 ) have examined advanced countries, with the majority being from Europe. With this in view, this study aims to fill the literature gap, especially from the perspective of emerging countries like India. 3. Data and methodology 3.1 Data The list of (241) bankrupt companies in the “wholesale trade, retail trade, and repair of motor” sector is sourced from the Insolvency and Bankruptcy Board of India. The aim of the study is to predict or label a company as bankrupt or otherwise given the financial data of the company. This fits the description of the classification problem, which can be addressed using AI-ML models ( Pompe and Feelders, 1997 ). However, to deploy AI-ML models, the training dataset should contain adequate representations from both positive and negative classes, viz., bankrupt and non-bankrupt companies. The efficacy of AI-ML models to predict bankruptcy risks in the trade services sector a sample comprising 5527 firms from wholesale trade, retail trade, and repair of motor vehicles is considered due to data availability ( Table 1 ). Of these 5527 firms, 241 were bankrupt. Hence, to achieve a balanced dataset, SMOTE technique is used to create an oversample dataset comprising 5286 functional and 5286 bankrupt firms. The dataset was first split into training and testing subsets. The SMOTE technique was then applied solely to the training data to generate a balanced dataset for model training, ensuring no synthetic data contaminated the hold-out test set used for final evaluation. Table 1. Sector wise number of bankrupt and non-bankrupt firms. Sector Listed Non-listed Grand Total Non-bankrupt Bankrupt Non-bankrupt Bankrupt A - Agriculture, forestry and fishing 49 6 389 36 480 B - Mining and quarrying 35 3 181 6 225 C - Manufacturing 303 136 329 487 1255 D - Electricity, gas, steam and air conditioning supply 21 3 648 41 713 E - Water supply; sewerage, waste management and remediation activities 0 0 11 0 11 F - Construction 21 35 138 124 318 G - Wholesale and retail trade; repair of motor vehicles and motorcycles 774 34 4512 207 5527 H - Transportation and storage 73 7 849 39 968 I - Accommodation and Food service activities 62 3 408 19 492 J - Information and communication 272 14 1442 33 1761 K - Financial and insurance activities 748 37 2732 103 3620 L - Real estate activities 0 0 6 0 6 M - Professional, scientific and technical activities 74 6 719 21 820 N - Administrative and support service activities 121 8 1237 30 1396 O - Public administration and defence; compulsory social security 2 0 36 0 38 P - Education 19 2 119 4 144 Q - Human health and social work activities 44 2 311 12 369 R - Arts, entertainment and recreation 7 0 39 4 50 S - Other service activities 8 1 72 1 82 - Others 76 8 392 20 496 Grand Total 2709 305 14570 1187 18771 3014 15757 18771 The share of bankrupt to non-bankrupt companies is around 50:50 resulting in a dataset that is balanced on both positive and negative classes. This addresses the class imbalance issue which affects the efficacy and accuracy of the AI-ML models dealing with the classification problem 5 . The set of explanatory variables used in this study are given Table 2 , they include firm level financial variables and ratios drawn from similar studies in the domain of bankruptcy prediction. Further, we have also tried to predict the bankruptcy in the trade sector by dividing the sample on the basis of different business rules (liquidity, profitability, and firm asset size). Table 2. List of financial ratios/variables used as explanatory variables. S. No Financial ratio/variable Notation used 1 Profit before interest and taxes to interest expenses PBIT_INT 2 Cash flows to debt CF_D 3 Debt to total asses D_TA 4 Return on assets ROA 5 Profit margin PMN 6 Profit after tax to total assets PAT_TA 7 Quick ratio QR 8 Sales to working capital S_WC 9 Profit before interest and taxes to sales PBIT_S 10 Current ratio CR 11 Working capital to total assets WC_TA 12 Cash flows to total assets CF_TA 13 Asset turnover ATR 14 Asset growth AGR 15 Cash flows to sales CF_S 16 Sales to total assets S_TA 17 Revenue growth RGR 18 Total loans to total assets TL_TA 19 Profit growth PGR 20 Retained profit growth rate RPGR 3.2 Methodology - AI-ML models Generally using the labelled data, the supervisory machine learning models extracts the patterns from the training dataset. Further, supervisory machine learning is divided into two categories of algorithms: regression based and classification-based approaches. Regression based supervisory machine learning approach is used to predict the continuous variables whereas the classification based supervisory machine learning approach is used to predict the dichotomous/categorical variables. In this study, our interest variable is dichotomous in nature. Therefore, we focus on the classification type of supervisory machine learning approaches which are very popular in the bankruptcy prediction literature are Logistic Regression (LR), Random Forests (RF), Naïve Baye (NB), Gradient Boosting (GB), Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Decision Trees (DT), and one popular artificial intelligence technique such as Artificial Neural Networks (ANN or NN). Though, many algorithms are available within the supervisory machine learning category, we employ the aforementioned 8 AI-ML techniques on the basis of their popularity in the bankruptcy prediction literature and their explainability, training and prediction speed and ease of implementation. Further, in this study, we have chosen to test the efficiency of these 8 AI-ML models, slightly departing from the practice adopted in the literature, wherein the focus is on using a single technique or a couple of techniques. Alaka et al. (2018) did a systematic review of 49 articles for the use of AI-ML models for bankruptcy prediction. The authors note that, of the 49 studies under review, only 30 studies compared the performance of the bankruptcy predictions by the AI-ML models. Further, a few techniques viz., Support Vector Machines, Artificial Neural Networks, are compared more often than others (ibid). Contrary to this, the present study has consistently used the 8 AI-ML models viz., LR, RF, NB, GB, SVM, KNN, DT, and NNs, for bankruptcy predictions in the Indian case. The literature on the use of AI-ML models for classification problems in general and bankruptcy predictions in particular clearly underscores that no single model outperforms others ( Kumar and Ravi, 2007 ; Alaka et al., 2018 ; and Tanaka et al., 2019 ). Specifically, with reference to the bankruptcy prediction the performance of the models is found to be influenced by sample size, multicollinearity, underlying statistical distributions, computational ability etc. All the aforementioned models have relative strengths and weaknesses stemming from the underlying data and model requirements. Hence, to alleviate the issue relating to data all the models are tested on a single sample to compare the relative performance. While the chosen sample may be inherently favourable for certain models, given the fact that all models face similar training and testing conditions, the results can be fairly compared. Furthermore, the AI-ML models inherently present a trade-off between the result accuracy and transparency, with models like LR and DT offering better transparency than SVM and NN which have higher accuracy. Hence, to be agnostic to the choice between transparency and accuracy, the analytical framework of this study presents the performance metrics for the chosen 8 AI-ML models coherently, leaving the researcher or practitioner to make his or her choice based on the use-case at hand. Given the widespread use of these models in the literature, we omit technical details for the sake of brevity. 3.2.1 Model validation strategy To rigorously evaluate model performance and guard against overfitting, all models were subjected to a repeated k-fold cross-validation procedure. The SMOTE-balanced dataset was randomly partitioned into *k=10* subsets of approximately equal size. For each model, the training and evaluation process was repeated 10 times. In each iteration (or fold), a different subset was used as the hold-out test set, while the remaining nine subsets were combined for training (including the application of SMOTE only on the training fold to prevent data leakage). The performance metrics for each fold were recorded. The final performance metrics presented in the results (Accuracy, Precision, Recall, F1-Score, Specificity, AUROC) represent the mean values calculated across all 10 test folds. This process provides a robust and reliable estimate of the models’ predictive generalization capability on unseen data. 3.2.2 Model specifications and hyperparameters All AI-ML models were implemented in Python 3.9 using scikit-learn (version 1.3.0) and TensorFlow (version 2.10.0) for the neural network. The data was split into training (70%) and testing (30%) sets, with a fixed random seed (random_state=42) to ensure reproducibility. The following hyperparameters were used for each model, selected via grid search with 5-fold cross-validation on the training set: Logistic Regression (LR): Penalty=‘l2’, C=1.0, solver=‘lbfgs’, max_iter=1000. Random Forest (RF): n_estimators=500, max_depth=20, min_samples_split=5, min_samples_leaf=2, random_state=42. Gradient Boosting (GB): n_estimators=300, learning_rate=0.05, max_depth=5, subsample=0.8, random_state=42. Support Vector Machine (SVM): Kernel=‘rbf’, C=10, gamma=‘scale’. K-Nearest Neighbors (KNN): n_neighbors=5, weights=‘distance’, metric=‘euclidean’. Decision Tree (DT): max_depth=15, min_samples_split=10, min_samples_leaf=4, random_state=42. Naïve Bayes (NB): GaussianNB with default parameters. Artificial Neural Network (ANN): A sequential model with two hidden layers (64 and 32 neurons, ReLU activation), dropout rate of 0.3, output layer with sigmoid activation, compiled with Adam optimizer (learning_rate=0.001), binary cross-entropy loss, and trained for 100 epochs with batch size 32. All continuous features were standardized using StandardScaler. For imbalanced sub-samples, SMOTE was applied only on the training set to avoid data leakage. The code and full configuration files are available in the supplementary repository. 3.3 Performance metrics Literature establishes that the performance of classification models is evaluated through the construction of confusion matrices ( Kuhn and Johnson, 2013 ). These matrices are a cross tabulation of number of actual cases and predicted cases as given below. In general, the positive class refers to the variable of interest. In this case “crisis” period is a positive class, with “non-crisis” period being a negative class. The confusion matrices ( Table 3 ) are then used for computing metrics that enable comparison of the model performance. Table 3. Confusion matrix. Number of Instances Actual Positive Negative Predicted Positive True Positives (TP) False Positives (FP) Negative False Negatives (FN) True Negatives (TN) Accuracy is the primary metric for assessing AI-ML model performance in classification problems, representing the ratio of correct predictions to total instances. However, it doesn’t account for misclassification errors. To address this, metrics like precision, sensitivity (recall), and specificity are used in Table 4 . Precision measures the rate of true positive predictions out of all positive predictions, indicating the model’s ability to avoid false positives. Sensitivity (recall) captures the rate of true positive predictions out of all actual positives, indicating the model’s ability to identify positive instances accurately. The F1-score, the harmonic mean of precision and recall, balances these errors. Specificity measures the rate of true negative predictions out of all actual negatives, akin to sensitivity but for negative instances. AUROC (Area Under Receiver Operating Characteristic Curve) assesses the model’s accuracy in distinguishing between positive and negative classes by plotting sensitivity against 1-specificity. A higher AUROC indicates better model performance. These metrics provide a comprehensive evaluation of AI-ML models beyond simple accuracy. Table 4. Model performance metrics. Test metric Specification Accuracy TP+TN/ (TP+FP+FN+TN) Total correct predictions/ Total instances in the dataset Precision TP/ (TP+FP) Correct positive predictions/ Total positive predictions Recall TP/ (TP+FN) (Sensitivity) Correct positive predictions/ Total positive instances Specificity TN/(TN+FP) Correct negative predictions/ Total negative instances F1- Score 2*(Precision*Recall)/ (Precision +Recall) Harmonic mean of precision and recall 3.4 Application of business rules business overlay Although AI-ML models provide very good accuracy rates as compared to traditional econometric models, they fail to provide a convincing causative link between explanatory variables and the predicted variables. One of the key concerns using AI-ML models is that the models often function as a black box, wherein only inputs and outputs are visible to the user ( Guidotti et al., 2018 ). While some AI-ML models do provide some guidance regarding causation they fall short of establishing a formal relationship between explanatory and predicted variables ( Freitas, 2014 ). To this end, to improve the explainability of the models used, this study applies business rules to add context to the predictions made by the AI-ML models. Though this falls short of providing a definitive causal link, it can provide direction of likely impact on the predicted variable given the business rules. Also, financial regulators often stipulate dispensations to mitigate stressed firms to avoid bankruptcy or failure based on differential criteria regarding asset size, profitability, and liquidity positions etc. This allows the regulators to ensure that benefits of such dispensations are utilized by genuine firms under stress and avoid a one-size fits all approach ( RBI, 2020 , 2023 ) 6 . Hence, these business rules are framed using the conventional credit risk or investment analysis used by banks and fund houses for selecting or monitoring their investments. The study uses the following three business rules based on liquidity, profitability, and asset size position of the firms. 3.4.1 Liquidity based business rules One of the early warning signs about financial distress in a firm is mismanagement of liquidity, often resulting in default and distress precipitating in bankruptcies. Hence, bankers traditionally stipulate minimum levels of liquidity parameters to be achieved or maintained by the firms to get credit facilities. To illustrate, firms should have quick and current ratios of minimum 1.00 and 1.33 respectively, which signals that the current assets of the firm are adequately covering the current liabilities ( Venkatachalam and Natarajan, 2015 ). Therefore, bankers and investors are more likely to monitor such liquidity ratios and form an opinion about the firm’s financial health. Hence, the sample data is bifurcated into two sets (A and B) using the liquidity thresholds mentioned above. The firms with quick and current ratio above 1.00 and 1.33 are categorized as firms with healthy liquidity, while those below the liquidity thresholds are categorized as firms with liquidity issues. Subsequently, the AI-ML models are run on samples A (healthy liquidity firms) and B (weak liquidity firms) after removing the liquidity ratios from the explanatory variables. Such an assessment primarily considers the liquidity parameters which are key to decisioning by the banks and investors and then looks at the risks of bankruptcy. 3.4.2 Profitability based business rules Like liquidity ratios, another key early warning indicator that banks and investors look out for monitoring firms is their profitability. Generally, bankers and investors approach firms that are profit making differently from those that are incurring losses in terms of investment strategy. Hence, the sample dataset is bifurcated in two sub-sets (A and B) based on the profitability of the firms viz., profit making (ROA being positive) and loss making (ROA being negative). Subsequently, the AI-ML models are run on samples A (profit making firms) and B (loss making firms) to look out for risks of bankruptcy, beyond profitability. 3.4.3 Asset size based business rules Notwithstanding the profitability and liquidity status of the companies, another key decision parameter considered by banks and investors is the size of the firm i.e., total assets. The selection and application of credit risk techniques vary depending on the size of the firm. Small firms may be highly vulnerable to macro-economic shocks and pose high risks, while large firms can better withstand such risks, their failure can have very high costs for the banker or investor. Also, in the event of bankruptcy, for larger firms it may take longer to realize the fair value of the stranded assets than compared to smaller firms. Hence, it might be rational for a banker or investor to differentially approach the risk posed by small and large firms. Accordingly, the sample is bifurcated into four categories viz., A, B, C and D based on the asset size of the companies as given Table 5 below. Subsequently, the AI-ML models are run on samples A to D to assess the performance of models and explore the role of various explanatory variables on signalling bankruptcy of firms across asset size categories. Table 5. Classification of companies based on the size of the assets. Asset size condition Category Greater than ₹5,000 Crore A Between ₹1,000 and ₹5,000 Crore B Between ₹200 and ₹1,000 Crore C Lesser than ₹200 Crore D Applying AI-ML models on the bifurcated datasets based on business rules can provides three insights on predicting bankruptcies among manufacturing companies in India. First, it allows the researchers to assess the performance of AI-MLs models of the bifurcated datasets based on business rules and identify the best performing models for each sub-segment. Second, it can identify the key variables to signal the bankruptcy risks beyond the specified business criteria viz., liquidity, profitability, and asset size. Third, it enables discerning not so good companies from the companies that are seemingly good companies on the specified criteria. From an investment risk analysis standpoint, such decision-making insights can be very useful to protect investor interest. The analysis not only offers sharper insights on bankruptcy risks within good performing companies with healthy liquidity and profitability, but also provides a list of variables with high IV values to monitor for picking up the bankruptcy signals. This facilitates investor to apply a differentiated approach to assessing risk across firm categories and better understand business models and risks emanating from them 7 . 3.5 Class imbalance – SMOTE technique For better bankruptcy prediction, it is crucial that the dataset used for AI-ML models is balanced between positive (bankrupt firms) and negative (non-bankrupt firms) classes. A skewed dataset can lead to higher error rates, as the model may not learn adequately about both classes. While segmenting samples based on business rules offers decision-making insights, it can inadvertently create unbalanced datasets, impacting model performance. To address this, the study employs the Synthetic Minority Oversampling Technique (SMOTE) to generate a balanced dataset ( Kim et al., 2015 ; Le et al., 2018 ; Veganzones and Séverin, 2018 ; Ghatasheh et al., 2020 ; Smiti and Soui, 2020 ; Tumpach et al., 2020 ; Alam et al., 2021 ; Garcia, 2022 ; Papíková and Papík, 2022 ; Amirshahi and Lahmiri, 2024 ). SMOTE, a widely used data preprocessing method, corrects class imbalances by creating synthetic examples from the minority class based on the feature space rather than the data space ( Fernández et al., 2018 ). This ensures the AI-ML models are trained on a balanced dataset, improving their predictive accuracy. To correct class imbalances within the training data, the study employs the Synthetic Minority Oversampling Technique (SMOTE). Crucially, SMOTE was applied after segregating the hold-out test set, and synthetic examples were generated only from the feature space of the training data. This protocol prevents information leakage and ensures the test set remains pristine for an unbiased assessment of model generalizability. The study applies AI-ML models to datasets segmented by business rules and balanced using SMOTE, enhancing the model’s ability to predict bankruptcy accurately. 4. Results and Discussion 4.1 Performance of AI-ML models on full sample The efficacy of AI-ML models to predict bankruptcy risks in trade services sector a sample of comprising 5527 firms from wholesale trade, retail trade, and repair of motor vehicle is considered. Of these 5527 firms, 241 were bankrupt. Hence, to achieve a balanced dataset, SMOTE technique is used to create an oversample dataset comprising 5286 functional and 5286 bankrupt firms. Foremost, the correlation matrix of the given in Figure 1 indicates that sparse correlation amongst the explanatory variables, underscoring their usefulness in signalling bankruptcy risks. Subsequently, like in the case of manufacturing and construction firms, we deploy the same 8 AI-ML models on the full sample and followed by the testing on the sub-samples which are bifurcated on the basis of liquidity, profitability, asset size business rules. Further, the testing of AI-ML models in this chapter follows the same methodologies adopted for manufacturing and construction firms. Hence, for brevity, the extended discussions on model performance are not presented in this chapter. The focus is limited to identifying key models and the set of explanatory variables with high IV values and the results are presented hereunder. Figure 1. Correlation matrix of numeric features (trade services sector firms). The average cross-validated performance metrics of the AI-ML models tested on the full sample of firms are given in Table 6 . The models boast an average accuracy of around 80 per cent indicating that AI-ML models can be used for predicting bankruptcy risks in the trade services sector. Also, the AUROC scores of models is around 0.83 indicating reasonable discriminatory power of the models. Further, as compared to the performance of AI-ML models for manufacturing and construction firms, the accuracy and discriminatory power of the models is lower in case of trade service firms. However, the performance of random forest and neural network models in case of trade service firms stands out compared to other models. Other models like gradient boosting, k-nearest neighbours, decision trees also register decent performance levels, next only to random forest and neural network models in this sector. Furthermore, the usefulness of the models in discerning both the functional and bankrupt firms is also good. The F1-score for random forest model is 0.96 while that of the neural network model is 0.88 indicating balanced prediction performance. Table 6. Performance metrics of models for predicting bankruptcy considering all the financial factors. Model Accuracy Precision F1-Score Sensitivity Specificity AUROC Logistic Regression 0.61 0.61 0.61 0.61 0.62 0.69 Random Forest 0.96 0.96 0.96 0.98 0.94 0.99 Gradient Boosting 0.86 0.87 0.86 0.92 0.8 0.93 SVM 0.67 0.69 0.66 0.51 0.82 0.75 KNN 0.85 0.87 0.85 0.96 0.73 0.93 Decision Tree 0.88 0.88 0.88 0.91 0.85 0.88 Naive Bayes 0.55 0.59 0.48 0.9 0.2 0.6 Neural Network 0.88 0.88 0.88 0.94 0.82 0.94 On the basis the information value (IV) or weight of evidence which are available in Table 7 , for trade services firms, the top-5 variables with high IV are interest coverage (PBIT_INT), return on assets (ROA), debt to total assets (D_TA), profit to total assets (PAT_TA), and working capital to total assets (WC_TA). The explanatory variables wise, the information value is provided in Table 7 below. The indicators with high IV can perform the role of early warning indicators as they contain relatively higher information about the impending bankruptcy risks than other explanatory variables. Interestingly for trade service firms, the working capital to total assets is a key variable with high IV value. Working capital is more relevant for trading firms as they depend on stock in trade and try to optimize creditors and debtors to maximize their revenues. A typical trade service firm can be thought of as moving stocks in trade, purchasing and/or selling on credit. Such mismatches in sale realizations may necessitate higher working capital requirements. Table 7. Information values of the explanatory variables. Explanatory variable IV PBIT_INT 0.907 ROA 0.864 D_TA 0.681 PAT_TA 0.646 WC_TA 0.635 CR 0.586 PMN 0.502 QR 0.381 PBIT_S 0.327 S_WC 0.23 CF_D 0.22 RGR 0.188 RPGR 0.172 CF_TA 0.109 CF_S 0.103 S_TA 0.085 TL_TA 0.083 ATR 0.083 AGR 0.08 PGR 0.064 4.2 Performance of AI-ML models on bifurcated sample - Business rules As observed in case of manufacturing and construction firms, deployment of AI-ML models on the sub-samples bifurcated based on business rules (viz., liquidity, profitability, and asset size) yield interesting insights. Specifically, in terms of IVs of variables, the sub-samples have revealed differential relative impact of explanatory variables to signal bankruptcy risks. Hence, a similar exercise is carried out for the trade service firms. The overall sample is bifurcated into sub-samples using liquidity, profitability, and asset sized based business rules. Further, using SMOTE technique, the sub-samples are balanced. The business rule wise performance metrics of the AI-ML models is presented hereunder. 4.2.1 Performance of AI-ML models on bifurcated sample (liquidity ratios) The cross-validated performance metrics of the AI-ML models on the liquidity-based sub-samples are given in Tables 8 and 9 . The accuracy rates of some of the AI-ML models viz., random forest, gradient boosting, neural network in predicting bankruptcy risks for both firms with and without liquidity problems are above 85 percent. Also, the AUROC scores of these models are above 0.90 indicating strong discriminatory power of the models. The F1-scores of the models are also around 0.90 suggesting a balanced performance of the models. Interestingly, the IVs of the explanatory variables for the companies with and without liquidity issues vary divergently. For companies without liquidity issues, the profit margin, total loans to total assets, asset turnover, debt to total assets, cash flows to sales are the top 5 explanatory variables with higher IV values in Table 10 . Table 8. Performance metrics firms above liquidity threshold values of 1.33 and 1. Model Accuracy Precision F1-Score Sensitivity Specificity AUROC Logistic Regression 0.67 0.67 0.67 0.64 0.7 0.76 Random Forest 0.99 0.99 0.99 1 0.98 1 Gradient Boosting 0.98 0.98 0.98 1 0.96 0.99 SVM 0.79 0.79 0.79 0.83 0.75 0.86 KNN 0.9 0.91 0.9 0.98 0.81 0.96 Decision Tree 0.96 0.96 0.96 0.98 0.95 0.96 Naive Bayes 0.56 0.66 0.48 0.95 0.17 0.78 Neural Network 0.99 0.99 0.99 1 0.98 1 Table 9. Performance metrics firms below liquidity threshold values of 1.33 and 1. Model Accuracy Precision F1-Score Sensitivity Specificity AUROC Logistic Regression 0.62 0.62 0.61 0.66 0.57 0.68 Random Forest 0.95 0.95 0.95 0.96 0.94 0.99 Gradient Boosting 0.85 0.85 0.85 0.9 0.79 0.93 SVM 0.63 0.66 0.62 0.42 0.84 0.71 KNN 0.82 0.84 0.81 0.95 0.68 0.9 Decision Tree 0.85 0.85 0.85 0.87 0.84 0.85 Naive Bayes 0.54 0.61 0.45 0.94 0.14 0.61 Neural Network 0.83 0.83 0.83 0.87 0.78 0.9 Table 10. Comparison between various explanatory variables ranked in descending order of IV (IV A - liquidity ratios above threshold, IV B - liquidity ratios below threshold). Companies with healthy liquidity Companies with weaker liquidity Variable IV A Variable IV B PMN 0.636 PBIT_INT 0.4184 TL_TA 0.572 PAT_TA 0.3918 ATR 0.5317 RGR 0.3217 D_TA 0.5162 ROA 0.3146 CF_S 0.4465 PBIT_S 0.29 PBIT_S 0.4274 D_TA 0.2135 S_WC 0.4112 PMN 0.1908 S_TA 0.3871 WC_TA 0.1714 ROA 0.3724 RPGR 0.1708 PAT_TA 0.3507 S_TA 0.1255 CF_D 0.2981 ATR 0.1242 PGR 0.29 S_WC 0.1235 CF_TA 0.2829 CF_S 0.1169 AGR 0.2772 CF_D 0.1102 PBIT_INT 0.2491 AGR 0.1077 RGR 0.2151 CF_TA 0.1058 RPGR 0.1936 TL_TA 0.1056 WC_TA 0.1322 PGR 0.1012 4.2.2 Performance of AI-ML models on bifurcated sample (profitability ratios) The average cross-validated performance metrics of the AI-ML models on the sub-samples created using profitability-based business rules are given in Tables 11 and 12 . As can be observed from the performance metrics, the average accuracy of the AI-ML models on the sub-samples for profit making and loss-making companies is like that of the overall sample. The average accuracy of AI-MLs for profit making companies is around 84 per cent and for loss making companies the average accuracy is 77 percent. The average AUROC scores of the models are 0.90 for profit making companies and 0.83 for loss making companies. Indicating that AI-ML models have higher discriminatory power to discern bankrupt firms from functional firms in case of profit-making firms than in case of loss-making firms. Also, the average F1-scores of the models follow similar trends between profit- and loss-making firms. Overall, the performance metrics indicate that AI-ML models are performing better in case of profit-making trade service firms than in case of loss-making firms. Notwithstanding the above, the performance metrics of AI-ML models in this case i.e., profitability-based bifurcation is either comparable or better than the levels registered for the overall sample. Table 11. Performance metrics of models for companies which are in profit (ROA > 0). Model Accuracy Precision F1-Score Sensitivity Specificity AUROC Logistic Regression 0.72 0.72 0.72 0.76 0.68 0.77 Random Forest 0.98 0.98 0.98 0.99 0.97 1 Gradient Boosting 0.91 0.92 0.91 0.95 0.87 0.97 SVM 0.78 0.81 0.78 0.92 0.65 0.86 KNN 0.88 0.9 0.88 0.98 0.78 0.96 Decision Tree 0.92 0.92 0.92 0.93 0.92 0.92 Naive Bayes 0.55 0.65 0.46 0.95 0.15 0.76 Neural Network 0.95 0.95 0.95 0.98 0.92 0.97 Table 12. Performance metrics of models for companies which are in loss (ROA < 0). Model Accuracy Precision F1-Score Sensitivity Specificity AUROC Logistic Regression 0.66 0.67 0.66 0.76 0.56 0.72 Random Forest 0.95 0.95 0.95 0.96 0.93 0.98 Gradient Boosting 0.87 0.88 0.87 0.92 0.82 0.94 SVM 0.63 0.65 0.62 0.46 0.81 0.75 KNN 0.81 0.84 0.81 0.95 0.68 0.9 Decision Tree 0.87 0.87 0.87 0.91 0.83 0.87 Naive Bayes 0.55 0.61 0.48 0.92 0.18 0.61 Neural Network 0.88 0.89 0.88 0.95 0.81 0.93 The IVs of the explanatory variables for the sub-samples based on profitability business rules is given in Table 13 . As in the case of liquidity-based bifurcation, in this case too, the variables with high IVs vary for both the profit making and loss-making firms as compared to the overall sample. As observed earlier, for the overall sample, profit margin, return on asset, debt to total asset have highest information content in signalling bankruptcy. Followed by profit after tax to total asset and working capital to total asset. However, for the profit-making firms, the current ratio, interest margin, profit margin, profit to total assets, and working capital to total assets are the top 5 variables with highest IVs. Also, for the loss-making firms, the growth in retained profit, interest coverage, profit before interest and taxes to sales, revenue growth, and profit to total assets are the top 5 variables with highest IVs. It is interesting to note the differences between the set of top 5 variables for the profit- and loss-making firms and with that of the overall sample. While interest coverage and profit to total assets figure out as variables with high IVs for the overall sample, they also figure out in case of both profit- and loss-making firms. Thus, bifurcating the overall sample into sub-samples level provides useful insights. Table 13. Comparison between various explanatory variables ranked in descending order of IV (IV A - companies in profit, IV B - companies in loss). Profitable companies (ROA Positive) Profitable companies (ROA Negative) Variable IV A Variable IV B CR 0.7081 RPGR 0.5091 PBIT_INT 0.6174 PBIT_INT 0.4776 PMN 0.5932 PBIT_S 0.4312 PAT_TA 0.5771 RGR 0.4271 WC_TA 0.416 PAT_TA 0.3651 D_TA 0.3972 QR 0.2804 QR 0.3941 CR 0.2752 S_WC 0.3671 WC_TA 0.2574 TL_TA 0.277 D_TA 0.2537 CF_D 0.2057 CF_TA 0.2325 PGR 0.1821 AGR 0.2308 CF_TA 0.1588 S_WC 0.1959 RPGR 0.1416 CF_D 0.1779 ATR 0.1171 TL_TA 0.1721 AGR 0.1098 PGR 0.1539 PBIT_S 0.1089 S_TA 0.135 S_TA 0.1086 ATR 0.1289 CF_S 0.1008 PMN 0.1149 RGR 0.0936 CF_S 0.0777 4.2.3 Performance of AI-ML models on bifurcated sample (Asset size of the company) The overall sample is bifurcated into 4 sub-samples based on the asset size of the firms. This enables analysis of the performance of AI-ML models and to glean the relative importance of explanatory variables using IVs in signalling bankruptcy risks across firm sizes. Comparatively the average firm size of trade services firms is lower than that of the manufacturing or construction firms 8 . The cross-validated performance metrics of the models for companies with category A asset size are given in Tables 14 , 15 , 16 , and 17 . The IVs of the explanatory variables for the four sub-samples are given in Table 18 . From the performance metrics, it can be observed that the average accuracy rate of AI-ML models for categories A, B, C and D companies are at 83 percent, 79 percent, 81 percent, and 84 percent respectively, which is greater than the accuracy rate of 80 percent achieved for the overall sample. Likewise, the AUROC scores for the AI-ML models for category A, B, C and D companies are at 0.90, 0.86, 0.87, and 0.88 respectively as compared to AUROC score of 0.84 achieved for the overall sample. This represents an adequate discriminatory power for the models. Further, across categories of companies, random forest model has achieved accuracy rates of 94 percent to 98 percent and the AUROC scores range from 0.99 to 1.00. Thus, outperforming all other models across categories. Furthermore, neural networks have a high accuracy rate in the case of category D companies. Table 14. Performance metrics of models for companies with category A asset size. Model Accuracy Precision F1-Score Sensitivity Specificity AUROC Logistic Regression 0.74 0.74 0.74 0.71 0.78 0.84 Random Forest 0.95 0.95 0.95 0.98 0.91 0.99 Gradient Boosting 0.92 0.92 0.92 0.96 0.88 0.98 SVM 0.8 0.81 0.8 0.86 0.75 0.88 KNN 0.82 0.85 0.82 0.97 0.68 0.89 Decision Tree 0.88 0.88 0.88 0.9 0.85 0.88 Naive Bayes 0.62 0.68 0.59 0.9 0.34 0.78 Neural Network 0.9 0.92 0.9 0.99 0.82 0.95 Table 15. Performance metrics of models for companies with category B asset size. Model Accuracy Precision F1-Score Sensitivity Specificity AUROC Logistic Regression 0.67 0.67 0.67 0.68 0.67 0.74 Random Forest 0.94 0.94 0.94 0.96 0.92 0.99 Gradient Boosting 0.89 0.9 0.89 0.94 0.84 0.96 SVM 0.64 0.66 0.63 0.49 0.8 0.79 KNN 0.83 0.85 0.82 0.96 0.69 0.91 Decision Tree 0.86 0.86 0.86 0.89 0.83 0.86 Naive Bayes 0.58 0.7 0.51 0.96 0.21 0.7 Neural Network 0.89 0.89 0.89 0.95 0.82 0.95 Table 16. Performance metrics of models for companies with category C asset size. Model Accuracy Precision F1-Score Sensitivity Specificity AUROC Logistic Regression 0.7 0.71 0.7 0.61 0.79 0.77 Random Forest 0.96 0.96 0.96 0.98 0.93 0.99 Gradient Boosting 0.91 0.92 0.91 0.96 0.87 0.96 SVM 0.74 0.74 0.74 0.75 0.74 0.82 KNN 0.84 0.86 0.84 0.96 0.72 0.92 Decision Tree 0.89 0.89 0.89 0.91 0.87 0.89 Naive Bayes 0.53 0.6 0.44 0.94 0.12 0.64 Neural Network 0.9 0.9 0.9 0.95 0.85 0.95 Table 17. Performance metrics of models for companies with category D asset size. Model Accuracy Precision F1-Score Sensitivity Specificity AUROC Logistic Regression 0.68 0.68 0.68 0.68 0.68 0.74 Random Forest 0.98 0.99 0.98 1 0.97 1 Gradient Boosting 0.96 0.96 0.96 0.99 0.92 0.99 SVM 0.71 0.73 0.71 0.59 0.83 0.84 KNN 0.9 0.91 0.89 0.98 0.81 0.97 Decision Tree 0.94 0.94 0.94 0.97 0.91 0.94 Naive Bayes 0.57 0.69 0.5 0.96 0.18 0.57 Neural Network 0.97 0.97 0.97 0.99 0.96 0.99 Table 18. Comparison between various explanatory variables ranked in descending order of IV. Category A Category B Category C Category D Variable IV A Variable IV B Variable IV C Variable IV D PAT_TA 0.72 PBIT_INT 0.80 PAT_TA 0.86 PAT_TA 0.86 CR 0.65 ROA 0.75 CR 0.62 PMN 0.64 PBIT_S 0.61 PMN 0.66 PBIT_S 0.59 CR 0.61 PMN 0.52 D_TA 0.64 D_TA 0.58 ROA 0.56 PBIT_INT 0.50 PAT_TA 0.63 PBIT_INT 0.54 RGR 0.42 S_WC 0.50 CF_D 0.40 ROA 0.48 QR 0.40 QR 0.49 RGR 0.39 RGR 0.46 CF_S 0.39 ATR 0.47 CR 0.37 QR 0.46 AGR 0.37 RGR 0.47 AGR 0.36 PMN 0.45 RPGR 0.36 D_TA 0.43 QR 0.35 RPGR 0.44 S_WC 0.36 CF_D 0.40 S_WC 0.32 S_WC 0.38 PBIT_S 0.36 ROA 0.34 PBIT_S 0.31 PGR 0.37 TL_TA 0.35 AGR 0.34 RPGR 0.23 CF_D 0.32 PGR 0.32 PGR 0.30 PGR 0.21 CF_S 0.29 CF_D 0.27 TL_TA 0.24 CF_S 0.16 AGR 0.26 ATR 0.24 CF_S 0.22 ATR 0.14 TL_TA 0.18 D_TA 0.22 RPGR 0.20 TL_TA 0.05 ATR 0.16 PBIT_INT 0.21 The analysis of IV of explanatory variables across asset size categories of companies reveals interesting insights in the Table 19 . At the overall sample level, interest coverage, return on asset, debt to total asset is seen to be the foremost variables with high IV values. Among these variables, profit to total assets is among the top 5 variables with high IV across all firm types. This is followed by ROA which figures in the top 5 variables for firms in categories B, C, and D, while interest coverage is important for firms in categories A, B, and C. In contrast, debt to total assets is among the top 5 variables only in case of firms in categories B and C. For larger firms (A, B), profit margin is more relevant. While for smaller firms in category D, revenue growth along with profit margin are relevant. Variables like profit before interest and taxes to sales, and current ratio also among the top 5 variables with high IV values. Though there are common variables possessing high IVs both at the overall sample and bifurcated sample, it may be prudent for the investor to adopt a segmented approach to capture the bankruptcy risks in an efficient manner. Table 19. Relative IV of each explanatory variables across asset size categories overall sample. S. No Non-bifurcated Asset size category Full sample A B C D 1 PBIT_INT PAT_TA PBIT_INT PAT_TA PAT_TA 2 ROA CR ROA CR PMN 3 D_TA PBIT_S PMN PBIT_S CR 4 PAT_TA PMN D_TA D_TA ROA 5 WC_TA PBIT_INT PAT_TA PBIT_INT RGR 6 CR S_WC CF_D ROA QR 7 PMN QR RGR RGR CF_S 8 QR ATR CR QR AGR 9 PBIT_S RGR AGR PMN RPGR 10 S_WC D_TA QR RPGR S_WC 11 CF_D CF_D S_WC S_WC PBIT_S 12 RGR ROA PBIT_S PGR TL_TA 13 RPGR AGR RPGR CF_D PGR 14 CF_TA PGR PGR CF_S CF_D 15 CF_S TL_TA CF_S AGR ATR 16 S_TA CF_S ATR TL_TA D_TA 5. Conclusion The wholesale and retail trade service sector are one of the crucial segments in the economy. This sector has seen its fair share of bankruptcies (247 companies in the sample are from this sector). Hence, the analysis of the AI-ML models to predict bankruptcy risks is extended to this sector on the similar lines carried out for the manufacturing and construction sector. The performance of the AI-ML models at the level of the overall sample is like that of the results obtained in case of manufacturing and construction firms. Albeit the accuracy levels are slightly lower for the firms in the trade services sector. However, the average accuracy and AUROC scores are above 80 per cent and 0.80 representing the usefulness of AI-ML models in predicting bankruptcies in the trade service sector too. An analytical exercise to bifurcate the overall sample into sub-samples based on liquidity, profitability, and asset size-based business rules and test the efficacy of AI-ML models is also carried out for the trade service sector. Based on model accuracy and AUROC scores, random forest model stands out as the best performing model both for the overall sample and sub-samples across business rules. This is followed by neural networks, gradient boosting, and decision tree models. The interesting facet of the analysis stems from the observations on the information values of the explanatory variables indicating their relative importance to signal bankruptcy risks. The analysis of IVs of the explanatory variables at the level of overall sample indicates that interest coverage, return on assets, debt, profit, and working capital to total assets are the top 5 variables with highest IVs. However, when analysed at the level of sub-samples bifurcate based on business rules, the set of more relevant explanatory variables varies significantly across sub-samples. For firms with liquidity issues, revenue growth is more relevant, while for firms with healthier liquidity profit margin become more important. Similarly, for the profit-making firm’s current ratio and total loans to asset are more relevant contrasting with the loss-making firms where revenue growth and growth in retained profit becomes more important. Also, there are differences in the most relevant variables across firms’ size categories, with profit to total assets figuring out in the top 5 variables across size categories. The results indicate that the investors and stake holders stand to gain from a segmented approach to analyse the bankruptcy risks in using AI-ML models, without losing the predictive accuracy. Further, this approach provides insights on variables with relatively higher information content to signal bankruptcy risks, which may not be visible at an aggregate level. 6. Limitations and Future Research Directions 6.1 Limitation Data-Centric Limitations: Reliance on Historical Financial Statements: The models are trained on accounting data, which is backward-looking, subject to reporting lags, and may not capture imminent operational crises or qualitative management failures. Survivorship and Selection Bias: The sample of “non-bankrupt” firms includes only those that have survived and filed records. It may inadvertently exclude firms that quietly dissolved or were acquired under distress, potentially biasing the “healthy” profile. Sectoral Aggregation: While we focus on the trade services sector (NACE G), it aggregates heterogeneous sub-sectors (wholesale, retail, vehicle repair). Our models may not capture the unique risk drivers of each sub-sector due to data constraints preventing finer disaggregation. Model-Centric Limitations: The Interpretability-Accuracy Trade-off: While we use Information Value (IV) and business rules for insight, our best-performing models (e.g., Random Forest, Neural Networks) remain complex “black boxes.” We cannot fully articulate the precise, non-linear causal pathways leading to a bankruptcy prediction. Static vs. Dynamic Prediction: Our models are essentially point-in-time classifiers. They predict bankruptcy over a fixed horizon but do not constitute a real-time, continuously updating early warning system that monitors firms dynamically as new data streams in. External Validity Scope: The models are calibrated for the Indian trade sector. Their direct applicability to other emerging economies or sectors without re-calibration is uncertain, as institutional and macroeconomic contexts differ. Methodological Choice Limitation: While SMOTE is essential for handling imbalance, the synthetic samples, though based on feature space neighborhoods, are not real firms. This could potentially introduce noise or smooth over rare but critical distress patterns that exist in the original, sparse minority class. 6.2 Future research scope Integrating Alternative Data: Future studies could enrich models with unstructured data (news sentiment, management commentary, supply chain news) and real-time indicators (digital footprint, transaction flows) to complement financial ratios. Explainable AI (XAI) Applications: Employing techniques like SHAP (SHapley Additive exPlanations) or LIME on top of the best-performing black-box models could provide local and global explanations for predictions, enhancing trust and actionable insight for stakeholders. Dynamic Forecasting Models: Developing models that use sequential data (e.g., financial time series) with architectures like LSTMs (Long Short-Term Memory networks) could move from classification to dynamic probability forecasting. Granular Sub-sector Analysis: With richer data, future work could build separate, tailored models for wholesale, retail, and motor vehicle repair to uncover sub-sector-specific pathologies. Cross-Country Comparative Studies: Applying a similar methodological framework to the trade sectors of other emerging economies would allow for comparative analysis, distinguishing universal distress signals from context-dependent ones. Data availability statement Underlying data The dataset supporting the findings of this study has been deposited in the Figshare repository. To protect participant confidentiality, the data have been de-identified and are available for research purposes only. Figshare: Dataset for Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective. Dataset. https://doi.org/10.6084/m9.figshare.30392467 ( Desai, 2025 ). The project contains the following underlying data: • Data.xlsx Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). References Agrawal K, Maheshwari Y: Efficacy of industry factors for corporate default prediction. IIMB Manag. Rev. 2019; 31 (1): 71–77. Alaka HA, Oyedele LO, Owolabi HA, et al. : A framework for big data analytics approach to failure prediction of construction firms. Appl. Comput. Inform. 2018; 16 (1/2): 207–222. Alam TM, Shaukat K, Mushtaq M, et al. : Corporate bankruptcy prediction: An approach towards better corporate world. Comput. J. 2021; 64 (11): 1731–1746. Alfaro E, Gámez M, García N: Bankruptcy Prediction Through Ensemble Trees. Ensemble Classification Methods with Applications in R. 2018; pp. 97–118. 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Reserve Bank of India. 4 For simplicity, henceforth, the wholesale trade, retail trade and repair of motor vehicles together shall be referred to as trade services. 5 Class imbalance refers to the situations where either one of the target classes (positive or negative) variables dominates the dataset impacting the training of the algorithm, resulting in higher classification error rates. 6 The dispensation allowed by the Reserve Bank of India to mitigated COVID-19 related stress on borrowers adopted a differentiated approach for small and larger firms with criteria specified on financial ratios of firms. 7 Although several other business criteria can be applied to understand bankruptcy risks, the present study the attention is limited to the basic and intuitive measures to get across the usefulness of adopting such an analytical framework. 8 Reckoning the same, the firms are classified based on asset size into four categories viz., A (>INR 5000 Cr); B (INR 1000 to 5000 Cr); C (INR 200 to 1000 Cr) and D (less than INR 200 Cr) Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 14 Nov 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Economics and Finance, Birla Institute of Technology & Science Pilani - Hyderabad Campus, Hyderabad, Telangana, 500078, India 2 Department of Commerce, Manipal Academy of Higher Education, Manipal, Karnataka, India Nagaraju Thota Roles: Conceptualization, Formal Analysis, Writing – Original Draft Preparation Guruprasad Desai Roles: Conceptualization, Methodology, Writing – Review & Editing Sreenivasulu Puli Roles: Formal Analysis, Methodology A.C.V. Subrahmanyam Roles: Formal Analysis, Methodology, Supervision V N Vishweswarsastry Roles: Methodology, Resources, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 13 Jan 2026, 14:1251 https://doi.org/10.12688/f1000research.170279.2 version 1 Published: 14 Nov 2025, 14:1251 https://doi.org/10.12688/f1000research.170279.1 Copyright © 2026 Thota N et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Thota N, Desai G, Puli S et al. Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.12688/f1000research.170279.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 13 Jan 2026 Revised Views 0 Cite How to cite this report: Gajdošíková D. Reviewer Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.194820.r463908 ) The direct URL for this report is: https://f1000research.com/articles/14-1251/v2#referee-response-463908 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 12 Mar 2026 Dominika Gajdošíková , University of Zilina, Slovakia, Slovakia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.194820.r463908 The paper examines the effectiveness of Artificial Intelligence and Machine Learning (AI-ML) models in predicting corporate bankruptcy within India’s trade services sector. The study is based on a dataset of 5,527 companies, including 241 bankrupt firms, and employs eight ... Continue reading READ ALL The paper examines the effectiveness of Artificial Intelligence and Machine Learning (AI-ML) models in predicting corporate bankruptcy within India’s trade services sector. The study is based on a dataset of 5,527 companies, including 241 bankrupt firms, and employs eight machine learning models such as Random Forest, Gradient Boosting, Neural Networks, and Support Vector Machines. To address class imbalance, the authors apply the SMOTE technique and evaluate model performance using repeated k-fold cross-validation. The results suggest that AI-ML models are capable of predicting bankruptcy in the sector with relatively high accuracy, particularly when segmenting firms based on business rules related to liquidity, profitability, and asset size. However, despite these contributions, several areas for improvement were identified that limit the paper’s theoretical grounding, methodological transparency, and analytical clarity. 1. Although the paper claims to address a gap related to bankruptcy prediction in India’s trade services sector, the originality of the study is not sufficiently articulated. The introduction would benefit from a more precise explanation of how the present research extends existing AI-ML bankruptcy prediction frameworks beyond simply applying established models to a new dataset. The novelty should be clarified in terms of theoretical contribution, methodological innovation, or empirical insight. 2. The argument that the trade services sector is understudied is presented but not strongly substantiated through systematic comparison with existing studies. A more explicit positioning of this research relative to prior empirical studies would strengthen the originality claim. 3. The introduction briefly references the importance of trade services and the need for sector-specific models but lacks a comprehensive overview of previous bankruptcy prediction research in both developed and emerging markets. The discussion of foundational studies and recent AI-ML developments should be expanded to better contextualize the research problem. 4. While the literature review is organized thematically, many references are discussed descriptively rather than critically. There is limited synthesis of how the cited studies collectively inform the research gap. Additionally, the review focuses primarily on methodological developments rather than on sector-specific empirical findings. 5. Although the study employs eight AI-ML models, the theoretical justification for selecting these specific algorithms is relatively limited. The discussion focuses mainly on their popularity in the literature rather than on their suitability for the specific characteristics of the dataset. 6. While hyperparameters are listed, the methodological explanation of model training and tuning remains relatively brief. A clearer explanation of feature selection procedures, variable transformations, and potential multicollinearity issues would improve transparency. 7. The paper introduces twenty financial ratios as explanatory variables but does not sufficiently justify their inclusion based on theoretical or empirical literature. A more structured explanation of how these variables relate to bankruptcy risk would strengthen the methodological framework. 8. The use of business rules for segmentation (liquidity, profitability, asset size) is interesting but insufficiently justified theoretically. The rationale for the chosen thresholds (e.g., liquidity ratios) should be supported with references or empirical reasoning. 9. The results are generally presented clearly through performance metrics such as accuracy, precision, recall, F1-score, and AUROC. However, the interpretation of these results remains somewhat descriptive. A deeper discussion explaining why certain models perform better than others would improve the analytical depth. 10. The practical implications for stakeholders such as investors, creditors, and regulators are mentioned but remain relatively general. The paper would benefit from clearer examples of how the proposed models could be implemented in real-world credit risk assessment or financial monitoring systems. Given the focus on an emerging market context, the study could more explicitly discuss implications for financial regulation, early warning systems, or banking supervision. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Corporate finance; bankruptcy and financial distress prediction; financial risk assessment; quantitative methods and predictive analytics in economics; machine learning applications in financial modelling. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Gajdošíková D. Reviewer Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.194820.r463908 ) The direct URL for this report is: https://f1000research.com/articles/14-1251/v2#referee-response-463908 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Por LY. Reviewer Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.194820.r463907 ) The direct URL for this report is: https://f1000research.com/articles/14-1251/v2#referee-response-463907 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 05 Mar 2026 Lip Yee Por , Universiti Malaya, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.194820.r463907 This manuscript investigates bankruptcy prediction in India’s trade services sector (wholesale, retail, and motor vehicle repair) using a suite of eight AI-ML classifiers (LR, RF, NB, GB, SVM, KNN, DT, ANN), addressing class imbalance via SMOTE, and extending the analysis through business-rule-based segmentation (liquidity, profitability, asset size). ... Continue reading READ ALL This manuscript investigates bankruptcy prediction in India’s trade services sector (wholesale, retail, and motor vehicle repair) using a suite of eight AI-ML classifiers (LR, RF, NB, GB, SVM, KNN, DT, ANN), addressing class imbalance via SMOTE, and extending the analysis through business-rule-based segmentation (liquidity, profitability, asset size). The proposed method is interesting but there are several major concerns: 1. The manuscript states that 241 bankrupt firms were sourced from IBBI and merged with 5,527 trade-service firms, with a train/test split and cross-validation. However, the time horizon, reference year of financial ratios, and whether the task is one-year-ahead prediction (or contemporaneous classification) are not explicitly defined. Bankruptcy prediction is inherently temporal, and unclear timing can inflate performance if observations are mixed across years. Suggestion: Clearly specify (i) the observation period, (ii) how the “bankrupt” label aligns with the financial statement year (s), and (iii) whether the model predicts bankruptcy ahead of time (e.g., t–1 → t). If possible, add a temporal holdout validation (train on earlier periods, test on later periods) and report results alongside random splits. 2. The paper reports strong performance (e.g., Random Forest/ANN often near the top). Given the original class ratio (241 bankrupt vs 5,286 non-bankrupt), using SMOTE can be appropriate, but it also raises risks of over-optimistic metrics if not handled strictly within folds and with careful model selection. Suggestion: Provide a step-by-step, unambiguous description of the pipeline order: split → CV folds → scaling → SMOTE → training → testing. Additionally, report mean ± standard deviation across folds (not just means) and include confidence intervals for AUROC/F1 to demonstrate robustness. 3. The manuscript lists hyperparameters and states grid search was used. However, it is not entirely clear whether hyperparameter selection occurred inside the repeated cross-validation loop (nested CV) or outside it. If tuning uses information from the same folds used to report performance, results can be biased upward. Suggestion: Use nested cross-validation (inner loop for tuning, outer loop for reporting) or clearly state and justify why the current strategy does not leak information. At minimum, provide the tuning search space and confirm that tuning used training folds only. 4. Segmenting by liquidity/profitability/asset size and ranking variables using Information Value (IV) is practically appealing. However, IV is typically associated with scorecard/logit-style reasoning, and its interpretation alongside tree ensembles and neural networks needs clearer methodological grounding. Suggestion: Explain precisely how IV was computed (binning approach, WoE/IV procedure, thresholds for “high IV”) and add a complementary model-agnostic interpretation check (e.g., permutation importance). If you retain IV as the main interpretability layer, explicitly justify its validity for the models used and discuss limitations. 5. The manuscript uses many design elements—(i) eight classifiers, (ii) SMOTE balancing, (iii) standardization, (iv) segmentation rules, (v) IV-based early-warning indicators—yet it is not demonstrated which components drive the gains. Suggestion: Add ablation experiments such as: With vs without SMOTE (or alternative imbalance handling) Full feature set vs top-k IV features Aggregate modelling vs segmented modelling (show whether segmentation improves AUROC/F1 materially, not just changes IV rankings) This will validate the necessity and contribution of each component of the framework. 6. The manuscript compares multiple ML models and reports multiple metrics but claims about superiority should be supported by statistical comparisons (especially when differences are small for some metrics). Suggestion: Add statistical tests for paired model comparisons across folds (e.g., corrected resampled t-test or a nonparametric alternative). Also consider reporting rank-based comparison across folds (average ranks) rather than relying solely on point estimates. 7. The dataset is described as trade services (wholesale, retail, repair) but acknowledges limited granularity. This heterogeneity could drive model behaviour and variable importance. Suggestion: Strengthen the limitation discussion and—if data permits—add a robustness check for listed vs non-listed firms, or at least stratified evaluation to confirm the model is not primarily learning listing status or scale effects. If sub-sector granularity is unavailable, discuss how this may affect generalization and policy recommendations. 8. Avoid citation of non-peer-reviewed or weakly substantiated works. Suggestion: Ensure all references are from peer-reviewed sources. Avoid citing arXiv or preprints papers. Remove or replace them with authoritative journal or conference papers. 9. While the manuscript cites several strong foundational works on bankruptcy prediction and AI-ML techniques, it omits recent and relevant studies on imbalanced learning, hybrid machine learning strategies, and advanced optimization-based predictive modeling that are directly applicable to financial distress forecasting. The current literature review would benefit from incorporating more recent AI-driven approaches that address class imbalance, hyperparameter optimization, and robust validation in complex predictive settings. Suggestion: Incorporate and discuss the following to enrich the literature review and strengthen the methodological positioning of the study: Refer to reference 1: → Provides direct methodological relevance by addressing bankruptcy forecasting under severe class imbalance conditions using hybrid ML techniques. This study can strengthen the justification for using SMOTE and multiple classifiers in your framework. Refer to reference 2: → Demonstrates the importance of systematic hyperparameter optimization in improving model robustness and generalization, which is directly relevant to your multi-model comparison strategy. Refer to reference 3: → Highlights the value of temporal deep learning architectures for financial prediction problems, which can help position your work within broader AI-driven financial forecasting research. Refer to reference 4: → Illustrates the integration of attention mechanisms and hybrid LSTM architectures to enhance predictive discrimination, offering conceptual support for exploring more advanced deep architectures beyond standard ANN models. Incorporating these references would better position your work within the current state-of-the-art in AI-enabled predictive modeling, particularly in handling imbalanced datasets, optimizing model architectures, and strengthening methodological rigor in financial risk forecasting. Minor Concerns 10. Replace “supervisory machine learning” with “supervised machine learning,” and ensure consistent naming of models/metrics (e.g., recall vs sensitivity). 11. A careful proofreading pass is recommended to reduce repetition and improve readability. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes References 1. Ainan U, Por L, Chen Y, Yang J, et al.: Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset. IEEE Access . 2024; 12 : 9369-9381 Publisher Full Text 2. Li H, Govindarajan V, Ang T, Shaikh Z, et al.: MSPO: A machine learning hyperparameter optimization method for enhanced breast cancer image classification. DIGITAL HEALTH . 2025; 11 . Publisher Full Text 3. Ku C, Xiong J, Chen Y, Cheah S, et al.: Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market. Mathematics . 2023; 11 (11). Publisher Full Text 4. He Z, Yang J, Alroobaea R, Yee Por L: SeizureLSTM: An optimal attention-based trans-LSTM network for epileptic seizure detection using optimal weighted feature integration. Biomedical Signal Processing and Control . 2024; 96 . Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Por LY. Reviewer Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.194820.r463907 ) The direct URL for this report is: https://f1000research.com/articles/14-1251/v2#referee-response-463907 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: László V. Reviewer Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.194820.r449963 ) The direct URL for this report is: https://f1000research.com/articles/14-1251/v2#referee-response-449963 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 24 Jan 2026 Vasa László , Széchenyi István University, Győr, Hungary Approved VIEWS 0 https://doi.org/10.5256/f1000research.194820.r449963 The authors amended the paper accordingly, taking the reviewers' opinions and critics into consideration. I ... Continue reading READ ALL The authors amended the paper accordingly, taking the reviewers' opinions and critics into consideration. I find the paper suitable for indexing in its current for, no additional changes are required. Competing Interests: No competing interests were disclosed. Reviewer Expertise: economics and management I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT László V. Reviewer Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.194820.r449963 ) The direct URL for this report is: https://f1000research.com/articles/14-1251/v2#referee-response-449963 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 14 Nov 2025 Views 0 Cite How to cite this report: László V. Reviewer Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.187721.r435705 ) The direct URL for this report is: https://f1000research.com/articles/14-1251/v1#referee-response-435705 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 27 Dec 2025 Vasa László , Széchenyi István University, Győr, Hungary Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.187721.r435705 The research focuses on an actual topic. Predicting bankruptcy in any sector is an evergreen issue, so investigating it in the wholesale, repair and motor vehicle repair looks like an original idea. Basically, I like the paper's ... Continue reading READ ALL The research focuses on an actual topic. Predicting bankruptcy in any sector is an evergreen issue, so investigating it in the wholesale, repair and motor vehicle repair looks like an original idea. Basically, I like the paper's focus and flow, however, I feel some weaknesses: - In the introduction, the reason for this research should be better explained, involving more sources. - The literature review is too short, and incomplete; it should be extended significantly, especially involving more latest international sources published in top journals. - While I can accept the selected methodology, the proper introduction and explanation is missing, namely why the authors selected the given methodological toolset. - the limitations of the research are not highlighted. So, I recommend revising the manuscript accordingly. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: economics and management I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT László V. Reviewer Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.187721.r435705 ) The direct URL for this report is: https://f1000research.com/articles/14-1251/v1#referee-response-435705 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Jan 2026 Guruprasad Desai , Manipal Academy of Higher Education, Manipal, India 10 Jan 2026 Author Response 1. In the introduction, the reason for this research should be better explained, involving more sources. Response: Thank you for your constructive feedback regarding the need to strengthen the justification ... Continue reading 1. In the introduction, the reason for this research should be better explained, involving more sources. Response: Thank you for your constructive feedback regarding the need to strengthen the justification and contextual foundation in the introduction. We agree that a more thorough explanation of the research rationale, supported by additional sources, will enhance the manuscript's scholarly contribution and clarity. In response, we will expand the final paragraphs of the introduction to explicitly articulate the research gaps, theoretical motivation, and practical necessity for this study, drawing on relevant literature and empirical evidence. 2. The literature review is too short, and incomplete; it should be extended significantly, especially involving more latest international sources published in top journals. Response: Thank you for the insightful feedback regarding the literature review. We acknowledge that expanding the review to include a more comprehensive and up-to-date survey of international sources, particularly from top-tier journals, will strengthen the scholarly foundation and contextual relevance of our study. In response, we will significantly extend Section 2: Literature Review by integrating recent (2020–2024) high-impact research on AI/ML-based bankruptcy prediction across sectors and economies, with a specific focus on studies from leading finance, computational intelligence, and risk management journals. 3. While I can accept the selected methodology, the proper introduction and explanation is missing, namely why the authors selected the given methodological toolset. Response: Thank you for this constructive observation regarding the need for a clearer methodological justification. We agree that a more explicit rationale for the selection of the specific AI-ML models, performance metrics, and analytical framework (including SMOTE and business rules) would strengthen the methodological transparency and scholarly rigor of the paper. In response, we will enhance Section 3: Data and Methodology by adding a dedicated subsection that systematically outlines the reasoning behind each methodological choice, linking them directly to the research objectives, data characteristics, and established practices in the bankruptcy prediction literature. 4. The limitations of the research are not highlighted. Response: Thank you for this important observation regarding the need to explicitly acknowledge the limitations of our research. We agree that a candid discussion of the study's constraints is essential for scholarly integrity, contextualizes the findings, and provides valuable direction for future research. In response, we will add a dedicated subsection titled "Limitations and Future Research" within the Conclusion section. This will clearly and concisely outline the key limitations pertaining to data, methodology, and generalizability, while simultaneously proposing pathways for subsequent studies to address these constraints. Thank you again for this valuable comment. We believe this addition will provide necessary clarity and reinforce the robustness of our research work 1. In the introduction, the reason for this research should be better explained, involving more sources. Response: Thank you for your constructive feedback regarding the need to strengthen the justification and contextual foundation in the introduction. We agree that a more thorough explanation of the research rationale, supported by additional sources, will enhance the manuscript's scholarly contribution and clarity. In response, we will expand the final paragraphs of the introduction to explicitly articulate the research gaps, theoretical motivation, and practical necessity for this study, drawing on relevant literature and empirical evidence. 2. The literature review is too short, and incomplete; it should be extended significantly, especially involving more latest international sources published in top journals. Response: Thank you for the insightful feedback regarding the literature review. We acknowledge that expanding the review to include a more comprehensive and up-to-date survey of international sources, particularly from top-tier journals, will strengthen the scholarly foundation and contextual relevance of our study. In response, we will significantly extend Section 2: Literature Review by integrating recent (2020–2024) high-impact research on AI/ML-based bankruptcy prediction across sectors and economies, with a specific focus on studies from leading finance, computational intelligence, and risk management journals. 3. While I can accept the selected methodology, the proper introduction and explanation is missing, namely why the authors selected the given methodological toolset. Response: Thank you for this constructive observation regarding the need for a clearer methodological justification. We agree that a more explicit rationale for the selection of the specific AI-ML models, performance metrics, and analytical framework (including SMOTE and business rules) would strengthen the methodological transparency and scholarly rigor of the paper. In response, we will enhance Section 3: Data and Methodology by adding a dedicated subsection that systematically outlines the reasoning behind each methodological choice, linking them directly to the research objectives, data characteristics, and established practices in the bankruptcy prediction literature. 4. The limitations of the research are not highlighted. Response: Thank you for this important observation regarding the need to explicitly acknowledge the limitations of our research. We agree that a candid discussion of the study's constraints is essential for scholarly integrity, contextualizes the findings, and provides valuable direction for future research. In response, we will add a dedicated subsection titled "Limitations and Future Research" within the Conclusion section. This will clearly and concisely outline the key limitations pertaining to data, methodology, and generalizability, while simultaneously proposing pathways for subsequent studies to address these constraints. Thank you again for this valuable comment. We believe this addition will provide necessary clarity and reinforce the robustness of our research work Competing Interests: The authors declare that they have no competing interests to disclose. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Jan 2026 Guruprasad Desai , Manipal Academy of Higher Education, Manipal, India 10 Jan 2026 Author Response 1. In the introduction, the reason for this research should be better explained, involving more sources. Response: Thank you for your constructive feedback regarding the need to strengthen the justification ... Continue reading 1. In the introduction, the reason for this research should be better explained, involving more sources. Response: Thank you for your constructive feedback regarding the need to strengthen the justification and contextual foundation in the introduction. We agree that a more thorough explanation of the research rationale, supported by additional sources, will enhance the manuscript's scholarly contribution and clarity. In response, we will expand the final paragraphs of the introduction to explicitly articulate the research gaps, theoretical motivation, and practical necessity for this study, drawing on relevant literature and empirical evidence. 2. The literature review is too short, and incomplete; it should be extended significantly, especially involving more latest international sources published in top journals. Response: Thank you for the insightful feedback regarding the literature review. We acknowledge that expanding the review to include a more comprehensive and up-to-date survey of international sources, particularly from top-tier journals, will strengthen the scholarly foundation and contextual relevance of our study. In response, we will significantly extend Section 2: Literature Review by integrating recent (2020–2024) high-impact research on AI/ML-based bankruptcy prediction across sectors and economies, with a specific focus on studies from leading finance, computational intelligence, and risk management journals. 3. While I can accept the selected methodology, the proper introduction and explanation is missing, namely why the authors selected the given methodological toolset. Response: Thank you for this constructive observation regarding the need for a clearer methodological justification. We agree that a more explicit rationale for the selection of the specific AI-ML models, performance metrics, and analytical framework (including SMOTE and business rules) would strengthen the methodological transparency and scholarly rigor of the paper. In response, we will enhance Section 3: Data and Methodology by adding a dedicated subsection that systematically outlines the reasoning behind each methodological choice, linking them directly to the research objectives, data characteristics, and established practices in the bankruptcy prediction literature. 4. The limitations of the research are not highlighted. Response: Thank you for this important observation regarding the need to explicitly acknowledge the limitations of our research. We agree that a candid discussion of the study's constraints is essential for scholarly integrity, contextualizes the findings, and provides valuable direction for future research. In response, we will add a dedicated subsection titled "Limitations and Future Research" within the Conclusion section. This will clearly and concisely outline the key limitations pertaining to data, methodology, and generalizability, while simultaneously proposing pathways for subsequent studies to address these constraints. Thank you again for this valuable comment. We believe this addition will provide necessary clarity and reinforce the robustness of our research work 1. In the introduction, the reason for this research should be better explained, involving more sources. Response: Thank you for your constructive feedback regarding the need to strengthen the justification and contextual foundation in the introduction. We agree that a more thorough explanation of the research rationale, supported by additional sources, will enhance the manuscript's scholarly contribution and clarity. In response, we will expand the final paragraphs of the introduction to explicitly articulate the research gaps, theoretical motivation, and practical necessity for this study, drawing on relevant literature and empirical evidence. 2. The literature review is too short, and incomplete; it should be extended significantly, especially involving more latest international sources published in top journals. Response: Thank you for the insightful feedback regarding the literature review. We acknowledge that expanding the review to include a more comprehensive and up-to-date survey of international sources, particularly from top-tier journals, will strengthen the scholarly foundation and contextual relevance of our study. In response, we will significantly extend Section 2: Literature Review by integrating recent (2020–2024) high-impact research on AI/ML-based bankruptcy prediction across sectors and economies, with a specific focus on studies from leading finance, computational intelligence, and risk management journals. 3. While I can accept the selected methodology, the proper introduction and explanation is missing, namely why the authors selected the given methodological toolset. Response: Thank you for this constructive observation regarding the need for a clearer methodological justification. We agree that a more explicit rationale for the selection of the specific AI-ML models, performance metrics, and analytical framework (including SMOTE and business rules) would strengthen the methodological transparency and scholarly rigor of the paper. In response, we will enhance Section 3: Data and Methodology by adding a dedicated subsection that systematically outlines the reasoning behind each methodological choice, linking them directly to the research objectives, data characteristics, and established practices in the bankruptcy prediction literature. 4. The limitations of the research are not highlighted. Response: Thank you for this important observation regarding the need to explicitly acknowledge the limitations of our research. We agree that a candid discussion of the study's constraints is essential for scholarly integrity, contextualizes the findings, and provides valuable direction for future research. In response, we will add a dedicated subsection titled "Limitations and Future Research" within the Conclusion section. This will clearly and concisely outline the key limitations pertaining to data, methodology, and generalizability, while simultaneously proposing pathways for subsequent studies to address these constraints. Thank you again for this valuable comment. We believe this addition will provide necessary clarity and reinforce the robustness of our research work Competing Interests: The authors declare that they have no competing interests to disclose. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Adukpo TK and Abdulmumin-Butali N. Reviewer Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.187721.r435704 ) The direct URL for this report is: https://f1000research.com/articles/14-1251/v1#referee-response-435704 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 24 Dec 2025 Tobias Kwame Adukpo , University for Development Studies, Tamale, Ghana Netifatu Abdulmumin-Butali , Information Technology and Management, The University of Texas at Dallas (Ringgold ID: 12335), Texas, USA Approved VIEWS 0 https://doi.org/10.5256/f1000research.187721.r435704 The study demonstrates that AI-ML models can predict bankruptcy in India's trade service sector with Random Forest performing best but critical flaws must be fixed: 1. charity whether SMOTE was applied before/after train-test split to rule out data leakage, ... Continue reading READ ALL The study demonstrates that AI-ML models can predict bankruptcy in India's trade service sector with Random Forest performing best but critical flaws must be fixed: 1. charity whether SMOTE was applied before/after train-test split to rule out data leakage, 2. address suspiciously high accuracy rates through external validation or cross validation to prove results are not overfitted and 3. provide all model hyperparameters and implantation details for replicability. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Adukpo T, Bethel J: Impact of Macroeconomic Factors on Government Spending in Ghana. American Journal of Applied Statistics and Economics . 2025; 4 (1): 119-126 Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine learning and deep learning We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Adukpo TK and Abdulmumin-Butali N. Reviewer Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.187721.r435704 ) The direct URL for this report is: https://f1000research.com/articles/14-1251/v1#referee-response-435704 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Jan 2026 Guruprasad Desai , Manipal Academy of Higher Education, Manipal, India 10 Jan 2026 Author Response Response File Charity whether SMOTE was applied before/after train-test split to rule out data leakage Response: Thank you for raising this important methodological point regarding the potential ... Continue reading Response File Charity whether SMOTE was applied before/after train-test split to rule out data leakage Response: Thank you for raising this important methodological point regarding the potential for data leakage in the application of the Synthetic Minority Oversampling Technique (SMOTE). We appreciate the opportunity to clarify this aspect of our procedure and strengthen the manuscript. In response to the reviewer's concern, we confirm that in our study, SMOTE was applied exclusively to the training dataset after the train-test split was performed, thereby rigorously preventing any data leakage from the synthetic samples into the test set. The original, unmodified test set was preserved for the final evaluation of all models to ensure an unbiased assessment of their predictive performance on real, unseen data. This approach is a standard and crucial practice to maintain the integrity of model validation when addressing class imbalance. We will incorporate this clarification into the revised manuscript to enhance methodological transparency. The most appropriate location for this addition is within Section 3.1 (Data) and Section 3.5 (Class Imbalance – SMOTE Technique), where the data preparation and SMOTE application are described. 2. Address suspiciously high accuracy rates through external validation or cross validation to prove results are not overfitted Response: We thank the respected reviewer for this insightful and important comment regarding the validation of the high accuracy rates reported in our study. In this regard, we have added highlighted the following paragraph along with the other changes here and there in the revised article: 3.2.1 Model Validation Strategy To rigorously evaluate model performance and guard against overfitting, all models were subjected to a repeated k-fold cross-validation procedure. The SMOTE-balanced dataset was randomly partitioned into *k=10* subsets of approximately equal size. For each model, the training and evaluation process was repeated 10 times. In each iteration (or fold), a different subset was used as the hold-out test set, while the remaining nine subsets were combined for training (including the application of SMOTE only on the training fold to prevent data leakage). The performance metrics for each fold were recorded. The final performance metrics presented in the results (Accuracy, Precision, Recall, F1-Score, Specificity, AUROC) represent the mean values calculated across all 10 test folds. This process provides a robust and reliable estimate of the models' predictive generalization capability on unseen data. 3. Provide all model hyperparameters and implantation details for replicability Response: Thank you for the constructive feedback. We appreciate the reviewer’s suggestion to include all model hyperparameters and implementation details to enhance the reproducibility of our study. In response, we will add a new subsection titled “3.2.2 Model Specifications and Hyperparameters” under Section 3.2 (Methodology - AI-ML Models). This subsection will provide a detailed description of the software, libraries, hyperparameter settings, and training procedures used for each of the eight AI-ML models implemented in the study. The addition will ensure that other researchers can replicate our experiments precisely. Thank you again for this valuable comment. We believe this addition will provide necessary clarity and reinforce the robustness of our experimental design. Response File Charity whether SMOTE was applied before/after train-test split to rule out data leakage Response: Thank you for raising this important methodological point regarding the potential for data leakage in the application of the Synthetic Minority Oversampling Technique (SMOTE). We appreciate the opportunity to clarify this aspect of our procedure and strengthen the manuscript. In response to the reviewer's concern, we confirm that in our study, SMOTE was applied exclusively to the training dataset after the train-test split was performed, thereby rigorously preventing any data leakage from the synthetic samples into the test set. The original, unmodified test set was preserved for the final evaluation of all models to ensure an unbiased assessment of their predictive performance on real, unseen data. This approach is a standard and crucial practice to maintain the integrity of model validation when addressing class imbalance. We will incorporate this clarification into the revised manuscript to enhance methodological transparency. The most appropriate location for this addition is within Section 3.1 (Data) and Section 3.5 (Class Imbalance – SMOTE Technique), where the data preparation and SMOTE application are described. 2. Address suspiciously high accuracy rates through external validation or cross validation to prove results are not overfitted Response: We thank the respected reviewer for this insightful and important comment regarding the validation of the high accuracy rates reported in our study. In this regard, we have added highlighted the following paragraph along with the other changes here and there in the revised article: 3.2.1 Model Validation Strategy To rigorously evaluate model performance and guard against overfitting, all models were subjected to a repeated k-fold cross-validation procedure. The SMOTE-balanced dataset was randomly partitioned into *k=10* subsets of approximately equal size. For each model, the training and evaluation process was repeated 10 times. In each iteration (or fold), a different subset was used as the hold-out test set, while the remaining nine subsets were combined for training (including the application of SMOTE only on the training fold to prevent data leakage). The performance metrics for each fold were recorded. The final performance metrics presented in the results (Accuracy, Precision, Recall, F1-Score, Specificity, AUROC) represent the mean values calculated across all 10 test folds. This process provides a robust and reliable estimate of the models' predictive generalization capability on unseen data. 3. Provide all model hyperparameters and implantation details for replicability Response: Thank you for the constructive feedback. We appreciate the reviewer’s suggestion to include all model hyperparameters and implementation details to enhance the reproducibility of our study. In response, we will add a new subsection titled “3.2.2 Model Specifications and Hyperparameters” under Section 3.2 (Methodology - AI-ML Models). This subsection will provide a detailed description of the software, libraries, hyperparameter settings, and training procedures used for each of the eight AI-ML models implemented in the study. The addition will ensure that other researchers can replicate our experiments precisely. Thank you again for this valuable comment. We believe this addition will provide necessary clarity and reinforce the robustness of our experimental design. Competing Interests: The authors declare that they have no competing interests to disclose Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Jan 2026 Guruprasad Desai , Manipal Academy of Higher Education, Manipal, India 10 Jan 2026 Author Response Response File Charity whether SMOTE was applied before/after train-test split to rule out data leakage Response: Thank you for raising this important methodological point regarding the potential ... Continue reading Response File Charity whether SMOTE was applied before/after train-test split to rule out data leakage Response: Thank you for raising this important methodological point regarding the potential for data leakage in the application of the Synthetic Minority Oversampling Technique (SMOTE). We appreciate the opportunity to clarify this aspect of our procedure and strengthen the manuscript. In response to the reviewer's concern, we confirm that in our study, SMOTE was applied exclusively to the training dataset after the train-test split was performed, thereby rigorously preventing any data leakage from the synthetic samples into the test set. The original, unmodified test set was preserved for the final evaluation of all models to ensure an unbiased assessment of their predictive performance on real, unseen data. This approach is a standard and crucial practice to maintain the integrity of model validation when addressing class imbalance. We will incorporate this clarification into the revised manuscript to enhance methodological transparency. The most appropriate location for this addition is within Section 3.1 (Data) and Section 3.5 (Class Imbalance – SMOTE Technique), where the data preparation and SMOTE application are described. 2. Address suspiciously high accuracy rates through external validation or cross validation to prove results are not overfitted Response: We thank the respected reviewer for this insightful and important comment regarding the validation of the high accuracy rates reported in our study. In this regard, we have added highlighted the following paragraph along with the other changes here and there in the revised article: 3.2.1 Model Validation Strategy To rigorously evaluate model performance and guard against overfitting, all models were subjected to a repeated k-fold cross-validation procedure. The SMOTE-balanced dataset was randomly partitioned into *k=10* subsets of approximately equal size. For each model, the training and evaluation process was repeated 10 times. In each iteration (or fold), a different subset was used as the hold-out test set, while the remaining nine subsets were combined for training (including the application of SMOTE only on the training fold to prevent data leakage). The performance metrics for each fold were recorded. The final performance metrics presented in the results (Accuracy, Precision, Recall, F1-Score, Specificity, AUROC) represent the mean values calculated across all 10 test folds. This process provides a robust and reliable estimate of the models' predictive generalization capability on unseen data. 3. Provide all model hyperparameters and implantation details for replicability Response: Thank you for the constructive feedback. We appreciate the reviewer’s suggestion to include all model hyperparameters and implementation details to enhance the reproducibility of our study. In response, we will add a new subsection titled “3.2.2 Model Specifications and Hyperparameters” under Section 3.2 (Methodology - AI-ML Models). This subsection will provide a detailed description of the software, libraries, hyperparameter settings, and training procedures used for each of the eight AI-ML models implemented in the study. The addition will ensure that other researchers can replicate our experiments precisely. Thank you again for this valuable comment. We believe this addition will provide necessary clarity and reinforce the robustness of our experimental design. Response File Charity whether SMOTE was applied before/after train-test split to rule out data leakage Response: Thank you for raising this important methodological point regarding the potential for data leakage in the application of the Synthetic Minority Oversampling Technique (SMOTE). We appreciate the opportunity to clarify this aspect of our procedure and strengthen the manuscript. In response to the reviewer's concern, we confirm that in our study, SMOTE was applied exclusively to the training dataset after the train-test split was performed, thereby rigorously preventing any data leakage from the synthetic samples into the test set. The original, unmodified test set was preserved for the final evaluation of all models to ensure an unbiased assessment of their predictive performance on real, unseen data. This approach is a standard and crucial practice to maintain the integrity of model validation when addressing class imbalance. We will incorporate this clarification into the revised manuscript to enhance methodological transparency. The most appropriate location for this addition is within Section 3.1 (Data) and Section 3.5 (Class Imbalance – SMOTE Technique), where the data preparation and SMOTE application are described. 2. Address suspiciously high accuracy rates through external validation or cross validation to prove results are not overfitted Response: We thank the respected reviewer for this insightful and important comment regarding the validation of the high accuracy rates reported in our study. In this regard, we have added highlighted the following paragraph along with the other changes here and there in the revised article: 3.2.1 Model Validation Strategy To rigorously evaluate model performance and guard against overfitting, all models were subjected to a repeated k-fold cross-validation procedure. The SMOTE-balanced dataset was randomly partitioned into *k=10* subsets of approximately equal size. For each model, the training and evaluation process was repeated 10 times. In each iteration (or fold), a different subset was used as the hold-out test set, while the remaining nine subsets were combined for training (including the application of SMOTE only on the training fold to prevent data leakage). The performance metrics for each fold were recorded. The final performance metrics presented in the results (Accuracy, Precision, Recall, F1-Score, Specificity, AUROC) represent the mean values calculated across all 10 test folds. This process provides a robust and reliable estimate of the models' predictive generalization capability on unseen data. 3. Provide all model hyperparameters and implantation details for replicability Response: Thank you for the constructive feedback. We appreciate the reviewer’s suggestion to include all model hyperparameters and implementation details to enhance the reproducibility of our study. In response, we will add a new subsection titled “3.2.2 Model Specifications and Hyperparameters” under Section 3.2 (Methodology - AI-ML Models). This subsection will provide a detailed description of the software, libraries, hyperparameter settings, and training procedures used for each of the eight AI-ML models implemented in the study. The addition will ensure that other researchers can replicate our experiments precisely. Thank you again for this valuable comment. We believe this addition will provide necessary clarity and reinforce the robustness of our experimental design. Competing Interests: The authors declare that they have no competing interests to disclose Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 14 Nov 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 2 (revision) 13 Jan 26 read read read Version 1 14 Nov 25 read read Tobias Kwame Adukpo , University for Development Studies, Tamale, Ghana Netifatu Abdulmumin-Butali , The University of Texas at Dallas (Ringgold ID: 12335), Texas, USA Vasa László , Széchenyi István University, Győr, Hungary Lip Yee Por , Universiti Malaya, Kuala Lumpur, Malaysia Dominika Gajdošíková , University of Zilina, Slovakia, Slovakia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Gajdošíková D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 Mar 2026 | for Version 2 Dominika Gajdošíková , University of Zilina, Slovakia, Slovakia 0 Views copyright © 2026 Gajdošíková D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The paper examines the effectiveness of Artificial Intelligence and Machine Learning (AI-ML) models in predicting corporate bankruptcy within India’s trade services sector. The study is based on a dataset of 5,527 companies, including 241 bankrupt firms, and employs eight machine learning models such as Random Forest, Gradient Boosting, Neural Networks, and Support Vector Machines. To address class imbalance, the authors apply the SMOTE technique and evaluate model performance using repeated k-fold cross-validation. The results suggest that AI-ML models are capable of predicting bankruptcy in the sector with relatively high accuracy, particularly when segmenting firms based on business rules related to liquidity, profitability, and asset size. However, despite these contributions, several areas for improvement were identified that limit the paper’s theoretical grounding, methodological transparency, and analytical clarity. 1. Although the paper claims to address a gap related to bankruptcy prediction in India’s trade services sector, the originality of the study is not sufficiently articulated. The introduction would benefit from a more precise explanation of how the present research extends existing AI-ML bankruptcy prediction frameworks beyond simply applying established models to a new dataset. The novelty should be clarified in terms of theoretical contribution, methodological innovation, or empirical insight. 2. The argument that the trade services sector is understudied is presented but not strongly substantiated through systematic comparison with existing studies. A more explicit positioning of this research relative to prior empirical studies would strengthen the originality claim. 3. The introduction briefly references the importance of trade services and the need for sector-specific models but lacks a comprehensive overview of previous bankruptcy prediction research in both developed and emerging markets. The discussion of foundational studies and recent AI-ML developments should be expanded to better contextualize the research problem. 4. While the literature review is organized thematically, many references are discussed descriptively rather than critically. There is limited synthesis of how the cited studies collectively inform the research gap. Additionally, the review focuses primarily on methodological developments rather than on sector-specific empirical findings. 5. Although the study employs eight AI-ML models, the theoretical justification for selecting these specific algorithms is relatively limited. The discussion focuses mainly on their popularity in the literature rather than on their suitability for the specific characteristics of the dataset. 6. While hyperparameters are listed, the methodological explanation of model training and tuning remains relatively brief. A clearer explanation of feature selection procedures, variable transformations, and potential multicollinearity issues would improve transparency. 7. The paper introduces twenty financial ratios as explanatory variables but does not sufficiently justify their inclusion based on theoretical or empirical literature. A more structured explanation of how these variables relate to bankruptcy risk would strengthen the methodological framework. 8. The use of business rules for segmentation (liquidity, profitability, asset size) is interesting but insufficiently justified theoretically. The rationale for the chosen thresholds (e.g., liquidity ratios) should be supported with references or empirical reasoning. 9. The results are generally presented clearly through performance metrics such as accuracy, precision, recall, F1-score, and AUROC. However, the interpretation of these results remains somewhat descriptive. A deeper discussion explaining why certain models perform better than others would improve the analytical depth. 10. The practical implications for stakeholders such as investors, creditors, and regulators are mentioned but remain relatively general. The paper would benefit from clearer examples of how the proposed models could be implemented in real-world credit risk assessment or financial monitoring systems. Given the focus on an emerging market context, the study could more explicitly discuss implications for financial regulation, early warning systems, or banking supervision. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Corporate finance; bankruptcy and financial distress prediction; financial risk assessment; quantitative methods and predictive analytics in economics; machine learning applications in financial modelling. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Gajdošíková D. Peer Review Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.194820.r463908) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1251/v2#referee-response-463908 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Por L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 05 Mar 2026 | for Version 2 Lip Yee Por , Universiti Malaya, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia 0 Views copyright © 2026 Por L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript investigates bankruptcy prediction in India’s trade services sector (wholesale, retail, and motor vehicle repair) using a suite of eight AI-ML classifiers (LR, RF, NB, GB, SVM, KNN, DT, ANN), addressing class imbalance via SMOTE, and extending the analysis through business-rule-based segmentation (liquidity, profitability, asset size). The proposed method is interesting but there are several major concerns: 1. The manuscript states that 241 bankrupt firms were sourced from IBBI and merged with 5,527 trade-service firms, with a train/test split and cross-validation. However, the time horizon, reference year of financial ratios, and whether the task is one-year-ahead prediction (or contemporaneous classification) are not explicitly defined. Bankruptcy prediction is inherently temporal, and unclear timing can inflate performance if observations are mixed across years. Suggestion: Clearly specify (i) the observation period, (ii) how the “bankrupt” label aligns with the financial statement year (s), and (iii) whether the model predicts bankruptcy ahead of time (e.g., t–1 → t). If possible, add a temporal holdout validation (train on earlier periods, test on later periods) and report results alongside random splits. 2. The paper reports strong performance (e.g., Random Forest/ANN often near the top). Given the original class ratio (241 bankrupt vs 5,286 non-bankrupt), using SMOTE can be appropriate, but it also raises risks of over-optimistic metrics if not handled strictly within folds and with careful model selection. Suggestion: Provide a step-by-step, unambiguous description of the pipeline order: split → CV folds → scaling → SMOTE → training → testing. Additionally, report mean ± standard deviation across folds (not just means) and include confidence intervals for AUROC/F1 to demonstrate robustness. 3. The manuscript lists hyperparameters and states grid search was used. However, it is not entirely clear whether hyperparameter selection occurred inside the repeated cross-validation loop (nested CV) or outside it. If tuning uses information from the same folds used to report performance, results can be biased upward. Suggestion: Use nested cross-validation (inner loop for tuning, outer loop for reporting) or clearly state and justify why the current strategy does not leak information. At minimum, provide the tuning search space and confirm that tuning used training folds only. 4. Segmenting by liquidity/profitability/asset size and ranking variables using Information Value (IV) is practically appealing. However, IV is typically associated with scorecard/logit-style reasoning, and its interpretation alongside tree ensembles and neural networks needs clearer methodological grounding. Suggestion: Explain precisely how IV was computed (binning approach, WoE/IV procedure, thresholds for “high IV”) and add a complementary model-agnostic interpretation check (e.g., permutation importance). If you retain IV as the main interpretability layer, explicitly justify its validity for the models used and discuss limitations. 5. The manuscript uses many design elements—(i) eight classifiers, (ii) SMOTE balancing, (iii) standardization, (iv) segmentation rules, (v) IV-based early-warning indicators—yet it is not demonstrated which components drive the gains. Suggestion: Add ablation experiments such as: With vs without SMOTE (or alternative imbalance handling) Full feature set vs top-k IV features Aggregate modelling vs segmented modelling (show whether segmentation improves AUROC/F1 materially, not just changes IV rankings) This will validate the necessity and contribution of each component of the framework. 6. The manuscript compares multiple ML models and reports multiple metrics but claims about superiority should be supported by statistical comparisons (especially when differences are small for some metrics). Suggestion: Add statistical tests for paired model comparisons across folds (e.g., corrected resampled t-test or a nonparametric alternative). Also consider reporting rank-based comparison across folds (average ranks) rather than relying solely on point estimates. 7. The dataset is described as trade services (wholesale, retail, repair) but acknowledges limited granularity. This heterogeneity could drive model behaviour and variable importance. Suggestion: Strengthen the limitation discussion and—if data permits—add a robustness check for listed vs non-listed firms, or at least stratified evaluation to confirm the model is not primarily learning listing status or scale effects. If sub-sector granularity is unavailable, discuss how this may affect generalization and policy recommendations. 8. Avoid citation of non-peer-reviewed or weakly substantiated works. Suggestion: Ensure all references are from peer-reviewed sources. Avoid citing arXiv or preprints papers. Remove or replace them with authoritative journal or conference papers. 9. While the manuscript cites several strong foundational works on bankruptcy prediction and AI-ML techniques, it omits recent and relevant studies on imbalanced learning, hybrid machine learning strategies, and advanced optimization-based predictive modeling that are directly applicable to financial distress forecasting. The current literature review would benefit from incorporating more recent AI-driven approaches that address class imbalance, hyperparameter optimization, and robust validation in complex predictive settings. Suggestion: Incorporate and discuss the following to enrich the literature review and strengthen the methodological positioning of the study: Refer to reference 1: → Provides direct methodological relevance by addressing bankruptcy forecasting under severe class imbalance conditions using hybrid ML techniques. This study can strengthen the justification for using SMOTE and multiple classifiers in your framework. Refer to reference 2: → Demonstrates the importance of systematic hyperparameter optimization in improving model robustness and generalization, which is directly relevant to your multi-model comparison strategy. Refer to reference 3: → Highlights the value of temporal deep learning architectures for financial prediction problems, which can help position your work within broader AI-driven financial forecasting research. Refer to reference 4: → Illustrates the integration of attention mechanisms and hybrid LSTM architectures to enhance predictive discrimination, offering conceptual support for exploring more advanced deep architectures beyond standard ANN models. Incorporating these references would better position your work within the current state-of-the-art in AI-enabled predictive modeling, particularly in handling imbalanced datasets, optimizing model architectures, and strengthening methodological rigor in financial risk forecasting. Minor Concerns 10. Replace “supervisory machine learning” with “supervised machine learning,” and ensure consistent naming of models/metrics (e.g., recall vs sensitivity). 11. A careful proofreading pass is recommended to reduce repetition and improve readability. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes References 1. Ainan U, Por L, Chen Y, Yang J, et al.: Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset. IEEE Access . 2024; 12 : 9369-9381 Publisher Full Text 2. Li H, Govindarajan V, Ang T, Shaikh Z, et al.: MSPO: A machine learning hyperparameter optimization method for enhanced breast cancer image classification. DIGITAL HEALTH . 2025; 11 . Publisher Full Text 3. Ku C, Xiong J, Chen Y, Cheah S, et al.: Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market. Mathematics . 2023; 11 (11). Publisher Full Text 4. He Z, Yang J, Alroobaea R, Yee Por L: SeizureLSTM: An optimal attention-based trans-LSTM network for epileptic seizure detection using optimal weighted feature integration. Biomedical Signal Processing and Control . 2024; 96 . Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Por LY. Peer Review Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.194820.r463907) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1251/v2#referee-response-463907 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 László V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 24 Jan 2026 | for Version 2 Vasa László , Széchenyi István University, Győr, Hungary 0 Views copyright © 2026 László V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors amended the paper accordingly, taking the reviewers' opinions and critics into consideration. I find the paper suitable for indexing in its current for, no additional changes are required. Competing Interests No competing interests were disclosed. Reviewer Expertise economics and management I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) László V. Peer Review Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.194820.r449963) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1251/v2#referee-response-449963 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 László V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 27 Dec 2025 | for Version 1 Vasa László , Széchenyi István University, Győr, Hungary 0 Views copyright © 2025 László V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The research focuses on an actual topic. Predicting bankruptcy in any sector is an evergreen issue, so investigating it in the wholesale, repair and motor vehicle repair looks like an original idea. Basically, I like the paper's focus and flow, however, I feel some weaknesses: - In the introduction, the reason for this research should be better explained, involving more sources. - The literature review is too short, and incomplete; it should be extended significantly, especially involving more latest international sources published in top journals. - While I can accept the selected methodology, the proper introduction and explanation is missing, namely why the authors selected the given methodological toolset. - the limitations of the research are not highlighted. So, I recommend revising the manuscript accordingly. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise economics and management I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 10 Jan 2026 Guruprasad Desai, Manipal Academy of Higher Education, Manipal, India 1. In the introduction, the reason for this research should be better explained, involving more sources. Response: Thank you for your constructive feedback regarding the need to strengthen the justification and contextual foundation in the introduction. We agree that a more thorough explanation of the research rationale, supported by additional sources, will enhance the manuscript's scholarly contribution and clarity. In response, we will expand the final paragraphs of the introduction to explicitly articulate the research gaps, theoretical motivation, and practical necessity for this study, drawing on relevant literature and empirical evidence. 2. The literature review is too short, and incomplete; it should be extended significantly, especially involving more latest international sources published in top journals. Response: Thank you for the insightful feedback regarding the literature review. We acknowledge that expanding the review to include a more comprehensive and up-to-date survey of international sources, particularly from top-tier journals, will strengthen the scholarly foundation and contextual relevance of our study. In response, we will significantly extend Section 2: Literature Review by integrating recent (2020–2024) high-impact research on AI/ML-based bankruptcy prediction across sectors and economies, with a specific focus on studies from leading finance, computational intelligence, and risk management journals. 3. While I can accept the selected methodology, the proper introduction and explanation is missing, namely why the authors selected the given methodological toolset. Response: Thank you for this constructive observation regarding the need for a clearer methodological justification. We agree that a more explicit rationale for the selection of the specific AI-ML models, performance metrics, and analytical framework (including SMOTE and business rules) would strengthen the methodological transparency and scholarly rigor of the paper. In response, we will enhance Section 3: Data and Methodology by adding a dedicated subsection that systematically outlines the reasoning behind each methodological choice, linking them directly to the research objectives, data characteristics, and established practices in the bankruptcy prediction literature. 4. The limitations of the research are not highlighted. Response: Thank you for this important observation regarding the need to explicitly acknowledge the limitations of our research. We agree that a candid discussion of the study's constraints is essential for scholarly integrity, contextualizes the findings, and provides valuable direction for future research. In response, we will add a dedicated subsection titled "Limitations and Future Research" within the Conclusion section. This will clearly and concisely outline the key limitations pertaining to data, methodology, and generalizability, while simultaneously proposing pathways for subsequent studies to address these constraints. Thank you again for this valuable comment. We believe this addition will provide necessary clarity and reinforce the robustness of our research work View more View less Competing Interests The authors declare that they have no competing interests to disclose. reply Respond Report a concern László V. Peer Review Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.187721.r435705) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1251/v1#referee-response-435705 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Adukpo T et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 24 Dec 2025 | for Version 1 Tobias Kwame Adukpo , University for Development Studies, Tamale, Ghana Netifatu Abdulmumin-Butali , Information Technology and Management, The University of Texas at Dallas (Ringgold ID: 12335), Texas, USA 0 Views copyright © 2025 Adukpo T et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The study demonstrates that AI-ML models can predict bankruptcy in India's trade service sector with Random Forest performing best but critical flaws must be fixed: 1. charity whether SMOTE was applied before/after train-test split to rule out data leakage, 2. address suspiciously high accuracy rates through external validation or cross validation to prove results are not overfitted and 3. provide all model hyperparameters and implantation details for replicability. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Adukpo T, Bethel J: Impact of Macroeconomic Factors on Government Spending in Ghana. American Journal of Applied Statistics and Economics . 2025; 4 (1): 119-126 Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Machine learning and deep learning We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 10 Jan 2026 Guruprasad Desai, Manipal Academy of Higher Education, Manipal, India Response File Charity whether SMOTE was applied before/after train-test split to rule out data leakage Response: Thank you for raising this important methodological point regarding the potential for data leakage in the application of the Synthetic Minority Oversampling Technique (SMOTE). We appreciate the opportunity to clarify this aspect of our procedure and strengthen the manuscript. In response to the reviewer's concern, we confirm that in our study, SMOTE was applied exclusively to the training dataset after the train-test split was performed, thereby rigorously preventing any data leakage from the synthetic samples into the test set. The original, unmodified test set was preserved for the final evaluation of all models to ensure an unbiased assessment of their predictive performance on real, unseen data. This approach is a standard and crucial practice to maintain the integrity of model validation when addressing class imbalance. We will incorporate this clarification into the revised manuscript to enhance methodological transparency. The most appropriate location for this addition is within Section 3.1 (Data) and Section 3.5 (Class Imbalance – SMOTE Technique), where the data preparation and SMOTE application are described. 2. Address suspiciously high accuracy rates through external validation or cross validation to prove results are not overfitted Response: We thank the respected reviewer for this insightful and important comment regarding the validation of the high accuracy rates reported in our study. In this regard, we have added highlighted the following paragraph along with the other changes here and there in the revised article: 3.2.1 Model Validation Strategy To rigorously evaluate model performance and guard against overfitting, all models were subjected to a repeated k-fold cross-validation procedure. The SMOTE-balanced dataset was randomly partitioned into *k=10* subsets of approximately equal size. For each model, the training and evaluation process was repeated 10 times. In each iteration (or fold), a different subset was used as the hold-out test set, while the remaining nine subsets were combined for training (including the application of SMOTE only on the training fold to prevent data leakage). The performance metrics for each fold were recorded. The final performance metrics presented in the results (Accuracy, Precision, Recall, F1-Score, Specificity, AUROC) represent the mean values calculated across all 10 test folds. This process provides a robust and reliable estimate of the models' predictive generalization capability on unseen data. 3. Provide all model hyperparameters and implantation details for replicability Response: Thank you for the constructive feedback. We appreciate the reviewer’s suggestion to include all model hyperparameters and implementation details to enhance the reproducibility of our study. In response, we will add a new subsection titled “3.2.2 Model Specifications and Hyperparameters” under Section 3.2 (Methodology - AI-ML Models). This subsection will provide a detailed description of the software, libraries, hyperparameter settings, and training procedures used for each of the eight AI-ML models implemented in the study. The addition will ensure that other researchers can replicate our experiments precisely. Thank you again for this valuable comment. We believe this addition will provide necessary clarity and reinforce the robustness of our experimental design. View more View less Competing Interests The authors declare that they have no competing interests to disclose reply Respond Report a concern Adukpo TK and Abdulmumin-Butali N. Peer Review Report For: Predicting Bankruptcy in Wholesale, Retail, and Motor Vehicle Repair: An AI-ML Perspective [version 2; peer review: 2 approved, 2 approved with reservations] . F1000Research 2026, 14 :1251 ( https://doi.org/10.5256/f1000research.187721.r435704) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1251/v1#referee-response-435704 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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