{"ID":2823685,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.24747","arxiv_id":"2512.24747","title":"Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach","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.","short_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...","url_abs":"https://arxiv.org/abs/2512.24747","url_pdf":"https://arxiv.org/pdf/2512.24747v1","authors":"[\"Tim J. Boonen\",\"Xinyue Fan\",\"Zixiao Quan\"]","published":"2025-12-31T09:42:03Z","proceeding":"q-fin.RM","tasks":"[\"q-fin.RM\",\"cs.LG\"]","methods":"[]","has_code":false}