Model risk management under PRA SS1/23 and internal governance frameworks. Risk tiering, inventory management, ongoing monitoring obligations, and the documentation required to defend a model under challenge. 8 articles.
The first Python implementation of the asymptotic Gini drift test from Wüthrich et al. (2025). A proper z-test for ranking degradation — not a heuristic, not a threshold, a p-va...
A UK motor frequency model drifts after an upstream vehicle group reclassification. We show how insurance-monitoring's PSI, A/E ratios, and Gini drift test caught the problem be...
Evidently is excellent for generic ML monitoring. It doesn't do exposure-weighted PSI, Poisson A/E ratios, Gini drift testing, or anytime-valid sequential tests. For UK insuranc...
NannyML is the best general-purpose ML monitoring library for teams without ground truth labels. For insurance pricing, it doesn't do exposure-weighted PSI, segmented A/E ratios...
Insurance model monitoring in Python that understands exposure weighting, development lags, and Gini drift. Why Evidently and NannyML miss what matters for pricing, and what ins...
Fairlearn is excellent for classification fairness. It was not built for insurance pricing, the Equality Act 2010, or the FCA's specific concern: proxy discrimination in a multi...
EquiPy is a technically excellent fairness correction tool built on optimal transport theory, from Arthur Charpentier's group at UQAM. insurance-fairness is an FCA-focused proxy...
Alibi Detect is a solid general-purpose drift detection library. It doesn't do exposure-weighted PSI, segmented A/E ratios, Gini discrimination drift, or PRA SS3/17 regulatory f...