Explainable artificial intelligence for anti-money laundering

The XAI4AML (Explainable AI for Anti-Money Laundering) chair will explore how AI and explainability affect the optimal level of financial regulation, including how different levels of explainability and regulation may affect the costs and benefits associated with deploying AI-based solutions for anti-money laundering (AML) enforcement. Traditional approaches used by banks to fight money laundering are both costly (€20 billion per year in Europe), and relatively ineffective, being based on deterministic rule-based models. Current AML systems generate many false positives, while at the same time missing large amounts of truly suspicious transactions. Professional criminals use sophisticated techniques to disguise transfers as normal-looking transactions. AI can reduce false positives and bring about greater effectiveness by identifying otherwise invisible trends across large data sets. However, problems of explainability, together with regulatory uncertainty, are the main barriers to implementing AI in AML systems. My interdisciplinary chair, combining economics, law (with Winston Maxwell, Director Law & Technology) and AI/data science (Stéphan Clémençon, Professor Applied Mathematics), will contribute to the economic literature on the economics of financial regulation and financial crime, while at the same time contributing to an operational need for clarity on what constitutes and “explainable” AI system for AML. The results will have a positive impact on designers of AI-based AML systems (such as the French fintech Bleckwen, partner of the chair), on users of the systems (such as banks and consulting firms, in particular PWC specialized in banking operations and compliance, partner of the chair), and on the financial regulator (ACPR, partner of the chair).