What happens after deployment. PSI, A/E ratios, champion/challenger testing, covariate shift detection, and performance degradation in live pricing. The discipline most teams under-invest in until something breaks. 12 articles.
How to detect when a motor book has hit the floor of its underwriting cycle — using PSI on new business mix, segment-level A/E, Gini stability, and mSPRT to know when the next m...
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...
FCA PS24/1 confirms enhanced Consumer Duty requirements from April 2026. EP25/2 flags ongoing fair value supervision in motor and home. No single technical checklist exists for ...
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...
Tutorial on monitoring insurance pricing models using actuarial KPIs. Gini tracking, segmented A/E, double-lift for champion/challenger. Why generic drift tools miss what matters.
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...
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...
Assumes familiarity with the Murphy decomposition framework. Focuses on the operational question: given a monitoring alert, how do you read GMCB vs LMCB...