Quantitative Analysis of Adaptive Behavior in Money Laundering Patterns to Avoid Detection

Financial institutions employ multiple resources to combat financial crimes such as money laundering and terrorism financing. Automated systems use combinations of rules or scenarios, value thresholds, peer group activity, rolling analysis of actual activity to historical activity, tolerances based on customer risk ratings, and often, artificial intelligence to identify atypical activity. Human intervention involves investigating, determining and documenting the rationales for closing an investigation without further escalation or reporting banks’ customers’ potentially suspicious behavior and transactional activity.