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.