Insurance Pricing Optimization via Off-Policy Evaluation
For actuaries and insurance companies, this work provides a data-driven pricing optimization framework that accounts for price sensitivity, though results are limited to synthetic data.
The paper formulates insurance pricing as a decision-making problem and proposes a kernelized inverse propensity score estimator for off-policy evaluation, achieving variance reduction. Using neural network-based policy optimization, they outperform existing techniques in a synthetic travel insurance environment.
Traditional insurance pricing relies on risk-based principles that ensure actuarial fairness and solvency but do not explicitly account for policyholders' price sensitivity. We formulate insurance pricing as a decision-making problem and study it using tools from off-policy evaluation and stochastic control. We propose a kernelized inverse propensity score estimator that exploits local structure in the action space and yields variance reduction compared to the classical inverse propensity score estimator. Building on these value estimates, we investigate policy optimization and present two practical approaches for computing optimal pricing rules: an interpretable data-shared Lasso formulation and a flexible policy parameterization based on neural networks. Using a controlled synthetic travel insurance environment, we empirically confirm the theoretical results and show that neural networks outperform existing techniques for policy optimization.