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.
Discrimination-free Insurance Pricing with Privatized Sensitive Attributes
Department of Risk and Insurance, University of Wisconsin-Madison（风险与保险系，威斯康星大学麦迪逊分校）；Department of Statistics and Operations Research, University of North Carolina-Chapel Hill（统计与运筹系，北卡罗来纳大学教堂山分校）
Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning
Institute of Statistics, Biostatistics and Actuarial Science（统计、生物统计与精算科学研究所）；UCLouvain（鲁汶大学）；Université du Québec à Montréal (UQAM)（魁北克大学蒙特利尔分校）；Université Libre de Bruxelles (ULB)（布鲁塞尔自由大学）
