When an algorithm influences millions of insurance decisions, governance is not optional.
Machine learning models in insurance touch coverage eligibility, premium pricing, claims severity prediction, and fraud scoring. Each of these applications carries regulatory, legal, and reputational risk if model behavior cannot be explained, audited, and validated against fairness standards.
State insurance regulators are increasingly issuing guidance or draft rules requiring carriers to document how predictive models work, test for proxy discrimination, and maintain human review processes for adverse underwriting or claims actions influenced by models.
Model risk management frameworks borrowed from banking are being adapted for the insurance context -- with inventory requirements, validation standards, and periodic monitoring built into the governance structure of any production-deployed model.
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