AI Bias Mitigation in Insurance Pricing and Claims Decisions

Abstract

Insurers are increasingly using artificial intelligence to underwrite, price, triage fraud and assess claims, although historical data and proxy variables can reinforce unjust inequalities among groups of customers. In this paper a, end-to-end framework that considers bias mitigation as an engineering and governance field is proposed. The bias entry-points of the lifecycle problem framing, data collection and labelling, feature design, training, decision thresholds, and human overrides and propose layer controls: (i) pre-processing audits, re-weighting, and proxy sanitization, (ii) in-processing approaches such as fairness-constrained optimization, adversarial debiasing, and monotonic/shape-constrained models, and (iii) post-processing (e.g. groupwise calibration, adjusting thresholds, and reject-option classification). Fairness is assessed by multi-metric dashboards (adverse impact ratio, statistical difference of parity, error-rate balance, and calibration-between-groups) and counterfactual tests on individuals. Incorporate explainability (global and local stories) and human in the loop review of edge cases which are operationalized through MLOps through versioned model cards, drift detection, audit trails, and stress tests. Comparative analysis depicts significant parity benefits (an increase in AIR, a decrease in parity and TPR differences) at a scale of small utility losses (AUC/MAE) without endangering actuarial plausibility. Place the approach in the context of the changing regulatory frameworks (e.g., NAIC principles, EU AI Act) and comment on the possible practical implication concerning compliance, customer confidence and resilience of portfolios. The outcome is that a repeatable process of achieving more equitable pricing and claims decision-making is possible without damaging economic performance