Using predictive modeling to combat money laundering

Abstract

Anti-Money Laundering (AML) is crucial for preventing financial crimes. Predictive modeling techniques can help identify fraudulent transactions. This paper introduces the IBM Synthetic Financial Data Money Laundering dataset containing millions of records with legitimate money laundering transactions. Objectives include exploring effectiveness, improving AML compliance, reducing false positives, and understanding consequences.