Explainable AI (XAI) for Enhancing Transparency in Money Laundering Risk Assessment
Itunuoluwa Adegbola · 2025
In the evolving landscape of financial crime, money laundering remains a persistent and increasingly complex challenge for institutions and regulators alike. With the adoption of artificial intelligence (AI) in anti-money laundering (AML) frameworks, significant progress has been made in automating risk detection and anomaly identification. However, the opacity of AI decision-making processes-often referred to as the "black box" problem-poses a barrier to trust, accountability, and regulatory compliance. Explainable AI (XAI) has emerged as a critical response to this challenge, providing transparency into how AI systems make predictions and enabling human stakeholders to understand, interpret, and validate those decisions. This article examines how XAI enhances transparency and accountability in money laundering risk assessment, improves model governance, and supports compliance functions in high-stakes financial environments. By exploring the intersection of machine learning, interpretability, and regulatory oversight, the discussion highlights how XAI can bridge the gap between innovation and institutional responsibility in the fight against illicit financial flows.