Agentic AI Governance Framework for Real-Time Fraud Detection in Digital Payment Systems: A Multi-Layered Architecture for Financial Security

Agentic AI Governance Framework for Real-Time Fraud Detection in Digital Payment Systems: A Multi-Layered Architecture for Financial Security | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 February 2026 V1 Latest version Share on Agentic AI Governance Framework for Real-Time Fraud Detection in Digital Payment Systems: A Multi-Layered Architecture for Financial Security Author : Jalendar Reddy Maligireddy 0009-0002-5881-0084 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177145217.78578198/v1 599 views 214 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The rapid proliferation of digital payment platforms has introduced complex, polymorphic fraud vectors that traditional rulebased detection systems struggle to address in real time. Simultaneously, the emergence of agentic artificial intelligence (AI) systems-autonomous software agents capable of perception, reasoning, and action-presents both transformative opportunities and governance challenges for the financial security domain. This paper proposes a comprehensive Agentic AI Governance Framework (AAGF) designed to orchestrate multi-agent systems for real-time fraud detection while ensuring regulatory compliance, explainability, and auditability. The framework integrates three principal layers: (1) a Perception Layer employing behavioral biometrics and transaction telemetry ingestion, (2) a Reasoning Layer utilizing ensemble models combining graph neural networks (GNNs) with large language model (LLM)-based anomaly reasoning, and (3) a Governance Layer implementing human-in-the-loop oversight, explainable AI (XAI) decision trails, and automated compliance reporting aligned with FinCEN Bank Secrecy Act (BSA) and EU Anti-Money Laundering Directive (AMLD6) requirements. Through architectural analysis and scenario-based evaluation across synthetic payment datasets simulating Faster Payments, FedNow, and UPI-class transaction volumes, we demonstrate that AAGF achieves a projected 91.3% fraud detection rate with a 58% reduction in false positives compared to baseline rule-based approaches, while maintaining full auditability. The paper further addresses the critical gap in AI governance literature by proposing a novel Agent Accountability Matrix (AAM) that maps autonomous decision authority to regulatory responsibility, offering financial institutions a deployable blueprint for responsible AI adoption in payment security infrastructure. Supplementary Material File (research_paper_1.pdf) Download 312.48 KB Information & Authors Information Version history V1 Version 1 18 February 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords agentic ai ai governance anti-money laundering digital payments explainable ai financial fraud detection multi-agent systems payment security real-time risk scoring regulatory compliance Authors Affiliations Jalendar Reddy Maligireddy 0009-0002-5881-0084 [email protected] Independent Researcher in Artificial Intelligence & Financial Crime Analytics Research Areas: Fraud Detection AI, Risk Monitoring Systems, Cloud-Native AI Platforms View all articles by this author Metrics & Citations Metrics Article Usage 599 views 214 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jalendar Reddy Maligireddy. Agentic AI Governance Framework for Real-Time Fraud Detection in Digital Payment Systems: A Multi-Layered Architecture for Financial Security. Authorea . 18 February 2026. DOI: https://doi.org/10.22541/au.177145217.78578198/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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