Artificial Intelligence for Compliance and Regulatory Reporting - Automating Anti-Money Laundering (AML) Detection in Financial Services

Abstract

Anti-Money Laundering (AML) detection is a critical regulatory and operational function for financial institutions. Traditional rule-based systems capture known typologies but produce high false positive rates, operational burden, and limited adaptive capacity against evolving threats. Recent advances in artificial intelligence (AI) and machine learning (ML) including supervised classifiers, unsupervised anomaly detection, graph learning, and explainable AI (XAI) provide the potential to transform AML operations: improving detection quality, prioritizing alerts, and automating regulatory reporting (e.g., suspicious activity reports, SARs). This paper presents a comprehensive, scholarly treatment of AI for AML: (1) a systematic problem formulation mapping AML tasks to ML objectives; (2) a detailed review of modeling approaches (statistical baselines, supervised learning, unsupervised methods, graph-based models, temporal sequence models, and hybrid systems); (3) mathematical formulations for core tasks (anomaly scoring, link prediction, temporal point process modeling, and risk scoring); (4) an end-to-end system architecture for production deployment with considerations for data engineering, latency, model governance, auditability, human-in-the-loop triage, and regulatory reporting workflows; (5) evaluation methodologies appropriate to highly imbalanced, non-stationary data (including offline metrics, backtesting, and controlled trials); and (6) ethical, legal, and operational concerns such as fairness, privacy, adversarial abuse, and interpretability. We include reproducible experiment blueprints, recommended feature sets, and practical recommendations for staged adoption. The manuscript synthesizes academic research and industry practice to deliver an actionable roadmap for institutions seeking to modernize AML through AI while maintaining regulatory compliance and operational resilience.