Explainable Artificial Intelligence for Anti-Money Laundering

Machine learning holds considerable promise for improving anti-money laundering efforts, but adoption has been slow due in part to regulatory demands for transparency. This white paper explores how explainable AI (XAI) techniques address the “black box” nature of ML models, enabling financial institutions to meet governance standards while benefiting from AI’s strengths. With increasing regulatory support for responsible AI, this paper provides practical guidance for building transparent, compliant and effective AML systems powered by machine learning.