Machine learning models embedded in insurance rating systems are drawing increasing regulatory scrutiny -- and carriers need to be prepared to explain and defend their models in ways that traditional actuarial filings did not require.
State insurance regulators are developing varying approaches to AI and ML model review. Some are extending existing rate filing review processes to require explainability documentation for ML-based rating factors. Others are developing dedicated AI oversight frameworks. The regulatory landscape is fragmented and evolving rapidly.
The explainability challenge is genuine. Some of the most predictive ML approaches -- gradient boosting, neural networks -- are less interpretable than traditional generalized linear models. Demonstrating that these models do not produce discriminatory outcomes, as required by insurance regulations, demands analytical approaches beyond what traditional actuarial filings addressed.
Carriers investing in model documentation, fairness testing, and explainability infrastructure now are building the compliance capability that will be required across more jurisdictions in coming years. Treating this as a future problem is a strategy that shortens the runway for response.
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