Artificial Intelligence for Money Laundering Detection

Abstract

This chapter provides an overview of artificial intelligence (AI) methods for anti-money laundering (AML) and discusses challenges. The increasing complexity of financial crime has exposed the limitations of traditional rule-based AML approaches. AI technologies like machine learning, natural language processing, and computer vision show promise for improving AML effectiveness and efficiency. However, AI also faces hurdles around data quality, model interpretability, ethics, and proper human-AI collaboration. The chapter reviews the state-of-the-art AI techniques being applied across AML domains including customer due diligence, transaction monitoring, risk scoring, and investigations. Key recommendations for implementing AI in practice involve extensive testing, explainable models, strong governance, and human-centered design focused on trust and transparency. While AI has limitations, thoughtful deployment focused on fairness, accountability, and empowering human expertise can allow financial institutions and regulators to realize its benefits for combating money laundering.