Smarter AML Detection: Data-Driven Rule Optimization with Analytics and Machine Learning

False positives remain one of the biggest challenges in Anti-Money Laundering (AML) monitoring, overwhelming compliance teams and slowing down investigations. This talk introduces a practical, analytics-driven framework for tuning AML detection rules to reduce noise without sacrificing risk coverage. By combining statistical calibration, customer segmentation, alert disposition analysis, and machine learning, we transform rigid rule-based monitoring into adaptive, risk-focused programs.

Through advanced techniques such as distribution analysis, time-series decomposition, clustering, and uplift modeling, attendees will learn how to uncover meaningful behavioral patterns, dynamically adjust thresholds, and improve alert quality. The session also highlights how explainable AI can enhance transparency and trust with regulators while maintaining operational efficiency.

Developers, data scientists, and compliance professionals will walk away with actionable strategies to modernize monitoring systems, leverage machine learning responsibly, and focus investigative resources where they matter most—on truly suspicious activity.