Maintaining both effective and efficient AML programs has proven elusive for financial institutions due to a reliance on legacy technology solutions and a seemingly ever-increasing investigator case workload. Artificial intelligence (AI) and machine learning, however, hold the key to helping institutions reduce regulatory risk, and improve the AML investigation process. This paper explains how transaction monitoring systems (TMS) can be enhanced with AI and machine learning to reduce risk and drive down AML costs.
A new report by Aite-Novarica (Aite) examines what’s driving transformation in anti-money laundering (AML) compliance. Specifically, the impact report examines the current AML ecosystem, key trends impacting financial institutions (FIs) and their AML compliance functions, and how they invest in technology and innovation to tackle today’s ever-evolving risk landscape.
As AI makes it possible for anti-money laundering (AML) processes to become increasingly automated, efficient, and effective, rules-based transaction monitoring systems (TMS) are being supplemented with these solutions to drive down false positives, streamline...
With an eye on what’s next for AML, we recently connected with Ken Harvey, former COO/CIO of HSBC Holdings, who outlined eight technology megatrends that are impacting AML compliance. We also look at how AML compliance technology is being developed, advanced, and...