Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach

• URL: http://arxiv.org/abs/2512.24747v1
• Date: Wed, 31 Dec 2025 09:42:03 GMT
• Title: Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach
• Authors: Tim J. Boonen, Xinyue Fan, Zixiao Quan,
• Abstract summary: Machine learning improves predictive accuracy in insurance pricing but exacerbates trade-offs between competing fairness criteria across different discrimination measures.<n>We propose a novel multi-objective optimization framework that jointly optimize all four criteria via the Non-dominated Sorting Genetic Algorithm II (NSGA-II)<n>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.
• Score: 1.529342790344802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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