Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917130419568640 |
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| author | Brauer, Alexej Menzel, Paul Wüthrich, Mario V. |
| author_facet | Brauer, Alexej Menzel, Paul Wüthrich, Mario V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04556 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing Brauer, Alexej Menzel, Paul Wüthrich, Mario V. Machine Learning Statistics Theory Statistical Finance Applications 62, 68 G.3 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. |
| title | Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing |
| topic | Machine Learning Statistics Theory Statistical Finance Applications 62, 68 G.3 |
| url | https://arxiv.org/abs/2510.04556 |