Curious if anyone's successfully integrated AI beyond basic rule-based systems for real-time AML flagging, especially for higher volume payment flows. What have the main hurdles been with false positives versus actual threat detection?
We've experimented with some unsupervised learning models for anomaly detection, but the regulatory burden of explaining why something was flagged makes it challenging to implement at scale. False positives are still a significant drain on resources.
We've experimented with more advanced AI for real-time AML, and the biggest hurdle has definitely been tuning the models to reduce false positives without missing critical anomalies. It's a constant balancing act between sensitivity and specificity, particularly with the sheer volume of transactions you're talking about.