Model drift, champion/challenger testing, and performance degradation in live pricing models. Coverage goes from PSI and A/E ratios to sequential testing, covariate shift detection, and building audit trails that satisfy the SMF holder.
Monthly peeking at champion/challenger results with a t-test inflates your false positive rate to ~25%. The mixture SPRT (Johari et al. 2022) is an e-process: valid at every int...
The Python equivalent of the IFoA MLR Working Party's R tutorial: Poisson GLM baseline, EBM GAM, and CatBoost GBM on UK motor data, with the full pipeline from data to governance.
Benchmark results on synthetic UK motor renewal books. The constrained optimiser outperforms flat rate changes on profit and retention simultaneously. What it does not do: fix a...
insurance-deploy provides the champion/challenger infrastructure, audit trail, and ICOBS 6B compliance tooling that MLflow does not. Here is how to use it.
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...
A practical walkthrough for pricing analysts: use insurance-causal for causal inference, insurance-conformal for prediction intervals, and insurance-monitoring for drift detecti...
Aggregate A/E at 0.94 looks fine. The model has been mispricing under-25s for eight months. Benchmark results on a synthetic UK motor book with three planted failure modes.
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...
What pricing actuaries actually monitor and how to do it in Python: Gini coefficient stability, A/E ratios by segment, exposure-weighted PSI, and double-lift curves using the in...
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...
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...
Evidently and NannyML are excellent tools. They do not understand exposure weighting, development lags, or the Gini drift test. insurance-monitoring does.
Champion/challenger testing is the right way to evaluate pricing model changes. Most teams do it badly or not at all — ad-hoc scripts, no audit trail, no...
How to run covariate shift detection as a recurring monthly check: monitoring cadence, ESS ratio trends, and the thresholds that trigger a retraining...
Champion/challenger with ICOBS 6B.2.51R compliance for UK insurers. SHA-256 routing, SQLite logging, bootstrap LR tests, SMF-signable report - insurance-deploy.
Correct covariate shift when acquiring an MGA book for UK motor pricing. Importance weighting, density ratio estimation, segment-level diagnostics - Python.
PRA SS1/23 requires quantitative pass/fail tests, not narrative. insurance-governance automates the full validation suite and generates auditable HTML reports.
GLMTransfer borrows statistical strength from a related source book to price thin target segments. Motor-to-fleet, home-to-landlord, and fleet roll-outs.
Champion/challenger testing is the right way to evaluate pricing model changes. Most teams do it badly or not at all — ad-hoc scripts, no audit trail, no...
The foundational walkthrough for insurance-covariate-shift: density ratio estimation, ESS/KL diagnostics, importance weighting, shift-robust conformal...
Assumes familiarity with the Murphy decomposition framework. Focuses on the operational question: given a monitoring alert, how do you read GMCB vs LMCB...
How to run covariate shift detection as a recurring monthly check: monitoring cadence, ESS ratio trends, and the thresholds that trigger a retraining...
PRA SS1/23 requires quantitative pass/fail tests, not narrative. insurance-governance automates the full validation suite and generates auditable HTML reports.
GLMTransfer borrows statistical strength from a related source book to price thin target segments. Motor-to-fleet, home-to-landlord, and fleet roll-outs.