用离线评估优化保险定价，兼顾公平与客户价格敏感性。
Insurance Pricing Optimization via Off-Policy Evaluation
- 基于逆倾向得分的核方法降低估计算法方差
- 神经网络策略在合成数据中显著优于传统方法
- 适合关注定价优化与公平性的保险科技研究者
传统保险定价依赖风险原则以保障精算公平与偿付能力，但未显式考虑投保人对价格的敏感性。本文将保险定价建模为决策问题，采用离线评估与随机控制工具进行研究。提出一种利用动作空间局部结构的核化逆倾向得分估计器，相比经典方法显著降低方差。基于这些价值估计，研究策略优化，提出两种实用的最优定价规则计算方法：可解释的数据共享Lasso模型与基于神经网络的灵活策略参数化。在受控的合成旅游保险环境中，实证验证了理论结果，表明神经网络在策略优化中优于现有技术。
原文摘要 · Abstract (English)
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.
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