Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach
This addresses fairness trade-offs in insurance pricing for regulators and insurers, though it is incremental as it builds on existing fairness-aware models.
The paper tackles the problem of balancing multiple fairness criteria in insurance pricing by proposing a multi-objective optimization framework using NSGA-II, which generates a Pareto front of trade-off solutions and consistently achieves a balanced compromise, outperforming single-model approaches like XGBoost and GLM.
Machine learning improves predictive accuracy in insurance pricing but exacerbates trade-offs between competing fairness criteria across different discrimination measures, challenging regulators and insurers to reconcile profitability with equitable outcomes. While existing fairness-aware models offer partial solutions under GLM and XGBoost estimation methods, they remain constrained by single-objective optimization, failing to holistically navigate a conflicting landscape of accuracy, group fairness, individual fairness, and counterfactual fairness. To address this, we propose a novel multi-objective optimization framework that jointly optimizes all four criteria via the Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating a diverse Pareto front of trade-off solutions. We use a specific selection mechanism to extract a premium on this front. Our results show that XGBoost outperforms GLM in accuracy but amplifies fairness disparities; the Orthogonal model excels in group fairness, while Synthetic Control leads in individual and counterfactual fairness. Our method consistently achieves a balanced compromise, outperforming single-model approaches.