In addition to concern around the cost and operational performance of transaction monitoring systems, there are doubts as to whether the information generated is fit for purpose.
Feedback on effective suspicious-activity submissions is a concern for many regulators.
“The regulatory bodies receive millions of suspicious-activity reports annually and how that data is utilized plays a large role in how effective the fight against financial crime is,” says Ted Sausen, director and anti-money laundering (AML) subject matter expert at Nice Actimize.
“It is estimated that less than 2% of laundered money is identified and seized,” he adds. “On the positive side, technologies such as artificial intelligence and machine learning are being implemented to enable better detection.”
Current regulation focuses on simply having compliance teams in place to file suspicious-activity reports rather than on the quality of those reports. In addition, compliance teams are burdened by the large number of alerts and reports, and often are not able to do a deep-dive analysis.
That is the view of Acuant chief product officer Jose Caldera, who suggests that focused analysis would yield more informative reports.
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