Detecting Financial Crimes: Evaluating the Efficacy of Automated Transaction Monitoring

Transaction monitoring systems (TMS) play a key role in a financial institution’s antimoney

laundering (AML) compliance program. The systems enable financial institutions

to monitor money/assets flows, using scenarios that analyze underlying transactions and

generate automated alerts for activities that may be indicative of money laundering1.

However, most TMS used in the financial industry today produce high false-positive

rates (approximately 90%-95% on average)2, resulting in high operational overhead and

missed opportunities to investigate high-value alerts.

This white paper discussed the AML risks, regulatory expectations, key transactional red

flags and case studies, as well as developed transaction monitoring scenarios for nine

AML topics. The nine topics were high risk jurisdictions, charitable organizations,

cash/ATM activities, lending products, marijuana, trade-based money laundering, elder

abuse, virtual currency, and human trafficking.

In addition, the paper discussed two emerging trends, namely, the implementation of

artificial intelligence technology and customer segmentation, to enhance the overall

quality of AML transaction monitoring.