Detecting predicate crimes for money laundering is highly challenging, and often requires significant time and resources. As investigators contend with overwhelming false positives from conventional systems, upcoming regulatory pressure from the Anti-Money Laundering Act of 2020 and FinCEN’s National Priorities is placing an emphasis on uncovering these activities effectively — adding complexity and cost to compliance.
This feature sheet explores how Nasdaq Verafin’s Targeted Typology Analytics analyze a range of behavioral, transactional, third-party and consortium insights for more effective detection of specific predicate crimes with fewer false positives and low alert to SAR ratios. Powered by artificial intelligence (AI) and continuously improved through feedback from Financial Intelligence Units, law enforcement, and investigators, our library of Targeted Typology Analytics deliver context-rich insights to help you pinpoint potential money laundering activity efficiently and effectively.
