On AML and AI: how are folks thinking about explainability?

asked by u/valentina_santos · 2d · 2 answers

Been diving deeper into AML regs, especially how new tech like AI fits in. We're exploring using some predictive models for transaction monitoring, but I keep hitting a wall on the 'explainability' requirement for regulatory scrutiny. With models becoming more black-box, how are you all approaching the need to clearly articulate why a particular transaction was flagged, especially when $QQQ moves are making things jumpy? Are there specific frameworks or tools being adopted to bridge that gap between model output and regulatory compliance?

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Top answers

  • u/fatou54· 6 pts· 2d

    This is a massive headache. We're currently building out a 'reason code' layer post-model to try and map AI outputs back to human-readable explanations, but it's a huge lift. I think regulators will eventually need to adapt, but for now, the burden is definitely on us to bridge that gap.

  • u/renzhou· 0 pts· 2d

    I agree, explainability is key. We're focusing on inherently more interpretable models where possible, even if it means slightly less predictive power. For critical alerts, we also mandate human review with a clear audit trail to supplement the AI's output, ensuring a verifiable explanation can always be provided.