# Insurance Pricing Optimization via Off-Policy Evaluation

arXiv: https://arxiv.org/abs/2605.28327 · HTML: https://mlsanity.com/paper/2605.28327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 8 of 8)

- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1604.00923/) (2016)
- [Doubly Robust Policy Evaluation and Optimization](https://mlsanity.com/paper/1503.02834/) (2015)
- [Model Distillation for Revenue Optimization: Interpretable Personalized Pricing](https://mlsanity.com/paper/2007.01903/) (2020)
- [Insurance pricing on price comparison websites via reinforcement learning](https://mlsanity.com/paper/2308.06935/) (2023)
- [Reinforcement Learning applied to Insurance Portfolio Pursuit](https://mlsanity.com/paper/2408.00713/) (2024)
- [Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data](https://mlsanity.com/paper/2202.10944/) (2022)

## Abstract (arXiv)

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.