提出保险定价模型的动态监控框架，解决数据漂移问题。
Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing
- 用偏差损失和穆尔菲评分分解评估模型校准度。
- 推导基尼得分渐近分布，构建其方差一致估计器。
- 集成排名漂移与校准测试，适配保险定价模型更新决策。
当保险组合及数据生成机制随时间演变时，保持定价模型预测性能极具挑战。聚焦非寿险领域，我们借鉴机器学习中的概念漂移术语，在精算场景中区分虚拟漂移与真实概念漂移。方法上，(i) 形式化偏差损失与穆尔菲评分分解，以评估全局与局部自校准；(ii) 研究基尼得分作为基于排序的性能指标，推导其渐近分布，并开发其渐近方差的一致自助估计器；(iii) 将上述结果整合为一个统计基础坚实、模型无关的监控框架，结合基于基尼得分的排名漂移检验与全局/局部自校准检验。通过在可控概念漂移场景下的修改型车险组合应用，验证了该框架如何指导模型重拟合或再校准的决策。
原文摘要 · Abstract (English)
Maintaining the predictive performance of pricing models is challenging when insurance portfolios and data-generating mechanisms evolve over time. Focusing on non-life insurance, we adopt the concept-drift terminology from machine learning and distinguish virtual drift from real concept drift in an actuarial setting. Methodologically, we (i) formalize deviance loss and Murphy's score decomposition to assess global and local auto-calibration; (ii) study the Gini score as a rank-based performance measure, derive its asymptotic distribution, and develop a consistent bootstrap estimator of its asymptotic variance; and (iii) combine these results into a statistically grounded, model-agnostic monitoring framework that integrates a Gini-based ranking drift test with global and local auto-calibration tests. An application to a modified motor insurance portfolio with controlled concept-drift scenarios illustrates how the framework guides decisions on refitting or recalibrating pricing models.
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