AI-DRIVEN ANTI-MONEY LAUNDERING AND REGULATORY AUTOMATION: A COMPREHENSIVE THEORETICAL FRAMEWORK FOR EFFECTIVE COMPLIANCE IN MODERN BANKING
Keywords:
Anti-money laundering, regulatory automation, artificial intelligence, explainability, compliance governanceAbstract
Background: The accelerating integration of artificial intelligence (AI) into financial services presents transformative opportunities for anti-money laundering (AML) and regulatory compliance. Across jurisdictions, regulatory bodies and financial institutions face twin pressures: the need to detect increasingly sophisticated financial crime and the obligation to comply with complex, evolving regulations. The present article synthesizes contemporary scholarship, practitioner reports, and technical resources to construct a rigorous theoretical framework describing how AI and machine learning (ML) can be operationalized to automate regulatory compliance and strengthen AML controls without sacrificing legal accountability or operational transparency (Adeyelu et al., 2024; Linh, 2024).
Objectives: This work aims to (1) systematically articulate the mechanisms through which AI augments AML detection and regulatory reporting, (2) critically evaluate trade-offs between automation, explainability, and regulatory acceptability, (3) propose a layered, governance-centric architecture for AI-enabled compliance, and (4) identify methodological and policy research gaps for future empirical study (Singh, 2025; Amblard-Ladurantie, 2024).
Methods: Employing a rigorous narrative synthesis and theory-building approach, this article integrates findings from peer-reviewed studies, technical reports, industry white papers, and practitioner blogs to derive testable propositions and an architecture for automated AML compliance. The methodology emphasizes cross-referencing of empirical and conceptual claims, critical evaluation of algorithmic methods, and normative analysis regarding governance, accountability, and operational deployment (Dias & Peters, 2020; Adeyelu et al., 2024).
Findings: AI enhances detection through layered capabilities: advanced feature engineering from transaction graphs, adaptive anomaly detection, supervised learning for typology classification, and natural language processing (NLP) for report generation and KYC (know-your-customer) data extraction (IEEE ICDM, 2020; Ethan, 2024). However, model risk — including adversarial vulnerability, bias amplification, and degradation over time — necessitates robust validation, human-in-the-loop review, and policy-oriented safeguards (Amblard-Ladurantie, 2024; Adeyelu et al., 2024). Practical deployment requires harmonizing technical design with regulatory expectations around explainability, auditability, and data governance (Bhawsar, 2020; Linh, 2024).
Conclusion: Responsible automation of AML and compliance is attainable but contingent on a governance-first architecture that aligns technical controls with legal standards and operational workflows. The article culminates in a prescriptive framework and a research agenda centered on evaluation metrics, interpretability methods, cross-jurisdictional harmonization, and socio-technical impact assessment (Singh, 2025; Aidoo, 2025).
References
Adeyelu, O. O., Ugochukwu, C. E., & Shonibare, M. A. (2024). AUTOMATING FINANCIAL REGULATORY COMPLIANCE WITH AI: A REVIEW AND APPLICATION SCENARIOS. Finance & Accounting Research Journal, 6(4), 580-601.
Ethan, E. (2024). Secure Algorithms: Enhancing Data Integrity in Banking through AI/ML.
Amblard-Ladurantie, C. (2024). How Artificial Intelligence Can Be Used in Compliance. Mega Blog. https://www.mega.com/blog/how-artificial-intelligence-can-be-used-compliance
AML Optimization Using AI. MagicFinserv. (2024). https://www.magicfinserv.com/ai-financial-services-industry/data-extractiontool/anti-money-laundering-aml-optimization-using-ai/
Bhawsar, A. (2020). An Indian AML Compliance Perspective. SignalX Blog. https://signalx.ai/blog/anti-money-launderingcompliance/
Linh, C. D. (2024). AI in Anti-Money Laundering: Revolutionizing Compliance in the Fight Against Financial Crime. SmartDev. https://smartdev.com/ai-in-anti-money-laundering-revolutionizing-compliance-in-the-fight-against-financialcrime/
Nabi, S. G., Aziz, M. M., Uddin, M. R., Tuhin, R. A., Shuchi, R. R., Nusreen, N., ... & Islam, M. S. (2024). Nutritional Status and Other Associated Factors of Patients with Tuberculosis in Selected Urban Areas of Bangladesh. Well Testing Journal, 33(S2), 571-590.
Anjum, R., Hafeez, M., & Sa’diah, K. (2024, September). The Impact of Social Media Use on Adolescent Well-Being and Academic Performance. In The Fourth International Conference on Innovations Social Sciences Education and Engineering (Vol. 4, pp. 045-045).
Dias, F. S., & Peters, G. W. (2020). A non-parametric test and predictive model for signed path dependence. Computational Economics, 56(2), 461-498.
Singh, V. (2025). Policy Optimization for Anti-Money Laundering (AML) Compliance using AI Techniques: A Machine Learning Approach to Enhance Banking Regulatory Compliance. International Journal of Engineering Research & Technology (IJERT), 14.
Medcalfe, D. (2024). Critical infrastructure in the face of global Cyber threats.
Ehlke, R., Salzer, T., & Westermeier, C. (2025). Increasing State Capacity through Central Bank Digital Currencies: A Comparative Account of the Digital Yuan and the Digital Rouble. In State, Capitalism, and Finance in Emerging Markets (pp. 231-254). Bristol University Press.
Aidoo, S. (2025). Evaluating the Effectiveness of AML Regulations: A Critical Review.
Ali, I., Ho, W., & Papadopoulos, T. (Eds.). (2025). Global Value Chains and Geopolitical Uncertainty: Disruption and Transformation. Taylor & Francis.
Borozna, A., & Kochtcheeva, L. V. (2024). War By Other Means. Springer Nature.
Aidoo, S. (2025). Case Studies of AML Compliance Failures: Lessons Learned and National Interest Implications.
KL Yung & EW Ngai (Year not specified). Application of machine learning techniques for combating financing for terrorism and money laundering.
S. Wang, S. Li, P. Zhao, Y. Zhang (Year not specified). Directions for anomaly detection in financial systems with expert systems and applications.
Z. Liu, Y. Li, Z. Wang (2020). A survey on adversarial learning in anti-money laundering. (Conference/Journal unspecified).
IEEE ICDM (2020). Mining Data on Conference International (ICDM): Transaction networks neural graph networks with transactions.
