What Happens When the Model Reads It Wrong: Building LLM Compliance Review That Survives an Audit

AML work teaches you early that the two kinds of compliance mistakes don't cost the same. A false alert costs an analyst an afternoon, while a missed violation can end up in a regulator's findings. I learned that while writing regulatory modules for a banking client in 2014, and it was still on my mind years later at Capital One, when my teams built AI-enabled OCR and LLM workflows for compliance review.

Those workflows reduced manual review effort by about 60%. Most of our engineering time went into the cases where the model misread a document and what the system should do next.

Twenty Years of Reading Rules for a Living

I didn't come to this through machine learning. My first decade was integration and data work: messaging systems, a payment-messaging gateway migration at a bank in Melbourne, and later AML scenario logic built on top of enterprise data marts....

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