自汽车保险中风险暴露的变化：一种加权 Tweedie 框架用于经验评级和取消罚金
Varying risk exposure in auto insurance: a weighted tweedie framework for experience rating an cancellation penalties
AI总结：
本文提出基于Tweedie框架的加权费率模型，考虑中期保单取消影响，通过灵活权重和暴露依赖的罚金结构，提升保费计算准确性，并展示该方法在保险中的战略优势。
摘要：
本文提出了一种新的 Tweedie 基础费率制定模型，该模型明确考虑中期保单取消的影响。利用加拿大保险公司提供的汽车保险数据，我们发现保单持有者在保单到期前取消保单的索赔经验存在显著差异。在经典的 Tweedie 框架基础上，我们引入了灵活的加权函数和依赖暴露水平的保费罚金结构，从而能够更真实地表示保单中断前的已赚保费。我们比较了 Tweedie 框架下的几种加权结构，并探讨了它们的理论性质，以及通过基于偏差的模型比较标准、从集中度和洛伦兹曲线衍生出的区域间曲线标准以及基于 Bregman 主导的 Murphy 图形进行实证性能评估。为了操作化所提出的模型，对罚金函数施加单调性和非负性约束，以确保与精算原则一致。最后，利用真实世界数据，我们展示了该方法在保险中的战略和竞争优势：它使保险公司能够通过取消罚金间接补偿大额损失，同时保持精算一致性和统计一致性。
英文摘要：
This paper proposes a new family of Tweedie-based ratemaking models that explicitly account for mid-term policy cancellations. Using an automobile insurance dataset from a Canadian insurer, we document a marked difference in claims experience between policyholders who maintain their coverage until maturity and those who cancel their policies mid-term. Building on the classical Tweedie framework, we introduce flexible weighting functions and a premium penalty structure that depend on the level of exposure, allowing for a more realistic representation of the earned premium when coverage is interrupted before the end of the policy period. We compare several weighting structures within the Tweedie framework and examine their theoretical properties, as well as their empirical performance using deviance-based model comparison criteria, an area-between-curves criterion derived from concentration and Lorenz curves, and Murphy diagrams grounded in Bregman dominance. To operationalize the proposed models, monotonicity and non-negativity constraints are imposed on the penalty function, ensuring consistency with actuarial principles. Finally, using real-world data, we show that this approach provides both a strategic and competitive advantage: it allows the insurer to indirectly compensate for large losses through a cancellation surcharge, while preserving actuarial coherence and statistical consistency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
