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.
Applying fairness constraints, calibration corrections, and drift monitoring as sequential post-hoc steps is how most UK pricing teams work. It is also architecturally broken. E...
SS1/23 is in active enforcement for banks. Its monitoring principles — operating boundaries, outcome analysis, documented escalation — are the benchmark the whole market is bein...
UK motor NCR is at 111% (EY Q4 2024). The market is at or near technical floor. Here is how to identify underpriced segments using loss cost trending, A/E monitoring, and Bühlma...
SS1/23 applies to banks, not insurers — but its monitoring principles are the benchmark auditors use regardless. This post maps the five SS1/23 validation principles to what a p...
How to set up insurance model monitoring in Python from scratch: PSI, Gini drift, and A/E with the insurance-monitoring library. Know when to redeploy, recalibrate, or refit.
Burger (arXiv:2512.23602, Dec 2025) applies conformal prediction to insurance model monitoring, replacing PSI > 0.2 and A/E > 1.15 with thresholds that are calibrated from data ...
PSI thresholds of 0.1 and 0.2 are industry convention, not statistical calibration. Burger (arXiv:2512.23602, Dec 2025) replaces them with conformal p-values, giving a distribut...
Brauer, Menzel & Wüthrich (arXiv:2510.04556) give us two things we have been missing: a formal hypothesis test for Gini drift and a Murphy score decomposition that tells you whe...
We fitted a Poisson GLM on the first third of freMTPL2 (677k French motor policies) and monitored it across two later temporal segments without refitting. PSI, A/E ratios with W...
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 Brauer et al. (2025). A proper z-test for ranking degradation — not a heuristic, not a threshold, a p-value.
EP25/2 (the FCA's evaluation of GIPP price-walking remedies) flags ongoing fair value supervision in motor and home. No single technical checklist exists for the pricing actuary...
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 Lloyd's MRM framework r...
Assumes familiarity with the Murphy decomposition framework. Focuses on the operational question: given a monitoring alert, how do you read GMCB vs LMCB...