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Navigating AML with AI/ML solutions in a dynamic regulatory landscape
Curious how others are tackling the escalating complexity of AML red flags, especially with the rapid evolution of crypto transactions. Are there any particular AI/ML solutions that are proving effective in catching new typologies without generating an overwhelming number of false positives? It feels like the regulatory guidance is constantly playing catch-up, and staying ahead operationally is a real challenge for PSPs.
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That's a very real challenge. We've had some success with unsupervised learning models that can identify anomalies in transaction patterns, but the ongoing calibration to minimize false positives is a significant time commitment. Have you found any particular features or data points that are proving most indicative in your crypto AML monitoring?