Evolving money laundering patterns are leading to huge fines and mounting pressure on FIs to become more vigilant. Learn how unsupervised machine learning and its inherent merits can help FIs to uncover hidden money laundering patterns and improve AML detection.
Today's AML & Compliance leaders face dual challenges of increasingly sophisticated digital financial crimes and the threat of growing fines from regulators. Learn how AI and Machine Learning can help FIs detect more crime and better triage alerts.
In this webinar recording, Catherine Lu from DataVisor and Keith Furst from Data Derivatives delved into real applications of how AI and ML help AML programs.
As mentioned in my previous articles, traditional rule-based transaction monitoring systems (TMS) have architectural limitations which make them prone to false positives and false negatives: Naive rules create a plague of false positives that are [...]
False positives have a terrible reputation among anti-money laundering (AML) circles. As mentioned in my previous article on ending the false positive alerts plague, approximately 90-95 percent of alerts generated by Transaction Monitoring Systems (TMS) [...]
Keith Furst is the Founder of Data Derivatives, and has years of experience within a variety of financial institutions including Tier One wholesale banks, investment banks, foreign bank branches, commercial banks, retail banks, broker-dealers, prepaid [...]