US mortgage lenders insure against artificial intelligence screening errors
This Financial Times report points at a familiar adoption curve: once AI moves from “decision support” into automated gatekeeping (screening applications, flagging fraud, routing edge cases), organizations start pricing the risk of getting it wrong.
The interesting part isn’t that underwriting models can err — it’s that lenders are reportedly purchasing insurance products explicitly tied to AI screening mistakes. That’s a strong signal that model governance is moving into the same category as cybersecurity and operational resilience: something you can’t just document, but may need to underwrite, monitor, and defend in court.
For teams building ML systems in regulated domains, it’s also a reminder that reliability work isn’t limited to accuracy. You need clear audit trails, measurable decision consistency over time, and guardrails for drift and data quality — because the failure mode isn’t “a bad prediction,” it’s denying (or approving) a loan incorrectly at scale.