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

TL;DR

This paper introduces a multi-objective optimization framework for insurance pricing that balances accuracy and multiple fairness criteria, providing diverse trade-off solutions using NSGA-II.

Contribution

It proposes a novel multi-objective approach to optimize accuracy and fairness simultaneously, overcoming limitations of single-objective models in insurance pricing.

Findings

XGBoost achieves higher accuracy but increases fairness disparities.

Orthogonal model excels in group fairness.

Synthetic Control performs best in individual and counterfactual fairness.

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.