Navigating AML Red Flags with AI - Experience Sharing

Evening all,

Been thinking a lot lately about the increasing sophistication of AML red flags, especially with the shift to more complex cross-border transactions and digital assets. It feels like the old rule-based systems are getting outpaced. We've been dabbling with some AI-driven solutions for transaction monitoring and anomaly detection, hoping to catch patterns human analysts might miss.

My question for the group, particularly those operating in multiple jurisdictions or dealing with a high volume of diverse transactions (like $BTC or other altcoins) is: what's been your experience with AI/ML in identifying genuine AML red flags versus generating a flood of false positives? Are there specific types of models or data approaches that you've found particularly effective in cutting through the noise and flagging truly suspicious activity, especially as regulatory guidance continues to evolve? I'm keen to hear practical insights on implementation and the inevitable challenges of model validation.