Fairness‐aware insurance pricing: A multi‐objective optimization approach

Abstract

Abstract Machine‐learning models can provide accurate predictions in insurance pricing, but can also increase disparities between protected groups. Existing fairness‐aware pricing approaches typically target one fairness notion at a time, making it difficult to compare trade‐offs between predictive accuracy, group fairness, individual fairness, and counterfactual fairness. We propose a multi‐objective framework for fairness‐aware insurance pricing. The framework combines several fairness‐aware base models and uses the Non‐dominated Sorting Genetic Algorithm II (NSGA‐II) to approximate the Pareto front over four objectives. We then use the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select a compromise solution from the Pareto front. Using two motor insurance datasets, we find that Extreme Gradient Boosting (XGBoost) improves predictive accuracy relative to the generalized linear model (GLM), but it can worsen some fairness metrics. The proposed ensemble provides a balanced compromise across the considered objectives and offers a favorable aggregate accuracy–fairness compromise.