Predictive Modeling Ethics and Fairness in Insurance

Why Fairness Matters

Predictive models can perpetuate or amplify existing biases in insurance pricing. Even when protected characteristics like race or gender are excluded, proxy variables such as zip code or credit score may produce disparate impacts. Regulators increasingly require insurers to demonstrate that models do not unfairly discriminate, making this a growing exam topic.

Fairness Metrics and Mitigation

Key fairness metrics include demographic parity, equalized odds, and calibration across groups. These metrics often conflict with each other, so actuaries must make principled trade-offs. Mitigation strategies include pre-processing (reweighting training data), in-processing (adding fairness constraints to the objective function), and post-processing (adjusting model outputs). For MAS-II, understand how to measure disparate impact ratios, explain the tension between actuarial accuracy and social fairness, and describe regulatory frameworks that govern the use of predictive models in insurance pricing.