Authors: Md Shehab Hossain, Saddam Nasir Chowdhury, Rakesh Kumar Reddy Aduri
Abstract: This study examines how transaction analytics can strengthen Anti-Money Laundering (AML) compliance and regulatory risk surveillance in an increasingly complex financial environment. Traditional rule-based monitoring systems often generate high volumes of false positives and may fail to detect sophisticated laundering techniques involving layered transactions, digital payment channels, shell entities, and cross-border fund movements. The research explores the integration of advanced analytics, machine learning, network analysis, and real-time monitoring to improve the identification of unusual transaction patterns and emerging financial-crime risks. It also evaluates how customer risk profiles, behavioral indicators, transaction histories, geographic exposure, and relationship networks can be combined to support more accurate risk scoring and alert prioritization. The study highlights the importance of explainable models, data quality, human oversight, privacy protection, and effective model governance in meeting regulatory expectations. Findings indicate that transaction analytics can reduce false positives, improve investigative efficiency, enhance suspicious activity detection, and enable financial institutions to adopt a more proactive and risk-based compliance approach. However, successful implementation depends on reliable data infrastructure, skilled compliance personnel, continuous model validation, and alignment with evolving regulatory requirements. The study concludes that transaction analytics should complement rather than replace expert judgment, providing institutions with a scalable and intelligent framework for improving AML effectiveness and regulatory risk surveillance in 2025 and beyond.
Keywords: Anti-money laundering (AML), Transaction analytics, Machine learning, Anomaly detection, Regulatory risk surveillance