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.

翻译：当保险组合与数据生成机制随时间演变时，维持定价模型的预测性能面临挑战。聚焦于非寿险领域，我们引入机器学习中的概念漂移术语，在精算情境下区分虚拟漂移与真实概念漂移。在方法论层面，我们（i）形式化偏差损失与Murphy评分分解以评估全局与局部自校准；（ii）研究基尼分数作为基于排序的性能度量，推导其渐近分布，并构建其渐近方差的一致自助法估计量；（iii）将上述结果整合为基于统计原理、模型无关的监控框架，该框架融合了基于基尼的排序漂移检验与全局及局部自校准检验。通过对引入受控概念漂移场景的改良车险组合进行实证应用，展示了该框架如何指导定价模型的重拟合或再校准决策。