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

arXiv: https://arxiv.org/abs/2510.04556 · HTML: https://mlsanity.com/paper/2510.04556/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [Automatically detecting data drift in machine learning classifiers](https://mlsanity.com/paper/2111.05672/) (2021)
- [Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff](https://mlsanity.com/paper/2310.12671/) (2023)
- [Model selection with Gini indices under auto-calibration](https://mlsanity.com/paper/2207.14372/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Practical Guide on Graphical Model Validation](https://mlsanity.com/paper/2609.26445/) (2026)

## Abstract (arXiv)

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.