Causal Machine Learning for Intervention Analysis in AML Systems: Beyond Correlation to Causation in Financial Crime Detection

Abstract

This article explores the paradigm shift from correlation-based to causation-based machine learning approaches in Anti-Money Laundering (AML) systems. We examine how causal machine learning enables more effective intervention analysis in financial crime detection, reducing false positives while increasing detection accuracy. Integrating causal inference frameworks with traditional ML methods provides financial institutions with more interpretable models that better withstand regulatory scrutiny and adapt to evolving criminal strategies. This paper presents theoretical foundations, implementation methodologies, and case studies demonstrating the practical advantages of causal approaches in AML systems.