The regulatory conversation about AI in insurance has moved from theoretical concern to active examination -- carriers using algorithmic models need to be ready.
Multiple state insurance departments have issued guidance or opened investigations into whether automated underwriting and pricing models produce outcomes that correlate with protected class characteristics, even when those characteristics are not directly used as inputs. Proxy variables are the focus.
Carriers that have invested in model explainability, disparate impact testing, and governance documentation are in a far stronger position during regulatory review than those who cannot clearly articulate how their models make decisions.
The cost of building algorithmic governance infrastructure is real but bounded. The cost of a regulatory finding of unfair discrimination is not.
#AIRegulation #InsuranceFairness #AlgorithmicBias #InsuranceCompliance #Underwriting
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