Proxy Discrimination in Insurance Pricing

A proxy is a variable that can carry information about another characteristic. In insurance pricing, location, purchasing behavior, credit-related variables, or other data may correlate with protected traits, even when race or another sensitive field is not directly supplied to a model. Removing explicit protected fields therefore does not by itself demonstrate that a pricing process is fair or legally permissible. Insurance regulation is jurisdiction- and product-specific. The NAIC’s model bulletin on insurers’ use of AI systems calls for a written program to govern systems that make or support decisions, including risk management, validation, documentation, and oversight. Model language and regulator guidance are not automatically binding law in every state; state adoption and other applicable rules matter. NAIC materials also discuss proxy discrimination as a concern where a data type or system predicts a protected characteristic rather than the insurance outcome. An audit should identify features, vendors, target outcomes, rating rules, and affected consumers; test whether variables have an actuarial basis; compare rates and decisions across relevant groups; and investigate missing data and geographic patterns. A disparity is a signal for investigation, not by itself a complete legal conclusion. Insurers should document corrective action and monitor changes over time. Consumers can ask the state insurance department about complaint and review options. Do not assume every correlated variable is prohibited or that a model is safe because it omits a sensitive field. The review should also ask whether apparently neutral variables are practical, accurate, and related to the insured risk in the product being priced. A feature with a plausible relationship in one line of coverage may be unsuitable in another. Keep a record of transformations and vendor updates so reviewers can reproduce how an input became a rating factor.