Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing

Battle Count: 80

The paper is primarily focused on non-life insurance pricing model monitoring. While the concept drift detection methodology (Gini-based ranking tests, calibration tests, bootstrap methods) could theoretically be adapted to monitor predictive models in quantitative trading, the specific application domain, data structures (exposure-weighted responses, claim frequencies), and business context (pricing stability, regulatory requirements) are distinctly actuarial. The model-agnostic nature of the framework provides some transferability.

The framework involves multiple components: computing the empirical Gini score with tie handling and case weights, bootstrap variance estimation, isotonic regression for recalibration, GLM balance correction, Murphy's score decomposition, and parametric bootstrap hypothesis tests. The mathematical derivations are rigorous but the algorithms are clearly specified. Practical implementation requires careful attention to data preparation (avoiding time-splitting pitfalls), consistent Gini score implementation, and appropriate significance level selection.

The paper uses the publicly available freMTPL2freq dataset, provides explicit algorithms (Algorithms 1-4), detailed mathematical formulations, and references to companion papers for implementation details (Brauer and Wüthrich 2025 for Gini score computation). The synthetic data generation process is fully described. However, no code repository is explicitly linked.