Money laundering accounts for an estimated 2–5% of global GDP annually. To combat this, financial institutions navigate a complex regulatory landscape where traditional rule-based systems often yield high false-positive rates. This webinar examines a machine learning framework for transitioning from rule-based detection to risk-based intelligent monitoring. The technical discussion focuses on strategies for handling extreme class imbalance in financial datasets while building explainable models.