The broader craft of pricing — beyond any specific method. Rate review process, technical price construction, multi-factor interaction, and the strategic questions that sit above the modelling. 5 articles.
Conformal risk control (Angelopoulos et al. ICLR 2024) requires monotone loss functions for its finite-sample guarantees. The Winkler score, two-sided regulatory tests, and capi...
A step-by-step tutorial on conformal prediction for insurance Python models, specifically the frequency-severity decomposition. Covers the calibration subtlety that breaks naive...
A stable Gini coefficient is not evidence that a model is performing well. It is evidence that the model is still ranking risks in the same order. Score decomposition separates ...
Liu & Meng's PowerBurr adds a fourth parameter to Burr XII that decouples body shape from tail heaviness. We explain the coupling trap, what the fix actually does, and when UK p...
The Bonferroni correction for joint frequency-severity prediction sets is conservative by construction. Braun et al. (arXiv:2507.20941) show that covariance whitening produces e...
PSI > 0.2 and A/E > 1.15 are industry folklore, not statistics. Conformal SPC replaces them with calibrated p-values that have a finite-sample false alarm rate guarantee, no nor...
Most pricing teams eyeball calibration plots or track PSI. Neither tells you whether observed miscalibration is statistically significant. ScoreDecompositionTest in insurance-mo...
When a challenger GBM outperforms the production GLM on deviance, the improvement is often entirely in miscalibration — meaning the GLM could be recalibrated to match without a ...
insurance-glm-tools v0.2.0 ships RobustMMDGLM — a Gamma GLM that automatically downweights large losses and selects features via L1, based on Kang & Kang (2026). Replaces ad-hoc...
Burr XII's body and tail are controlled by the same parameters — you can't fix one without breaking the other. Liu & Meng's PowerBurr adds a fourth parameter that decouples them...
insurance-monitoring v1.0.0 adds ModelMonitor with check_gmcb and check_lmcb — separate tests for global and local calibration drift, wired into a three-way REDEPLOY/RECALIBRATE...
A new mortality model from Liu & Zhou (2026) shows that cause-specific shocks decay heterogeneously — some fast, some slow. The analogy to UK claims inflation is exact, and the ...
Standard discrimination-free pricing applies to a single outcome. Income protection premiums are derived from a matrix of transition rates. Applying Lindholm to the aggregate pr...
The FCA has committed to evaluating how UK insurers use AI in pricing, underwriting, and claims. No new AI-specific rulebook is coming — but the regulator now expects evidence, ...
A practical comparison of Python and R for UK personal lines insurance pricing — data wrangling, GLMs, GBMs, deployment, and Databricks. Honest about where R still wins.
Reproduce an Emblem frequency-severity GLM in Python: factor tables, one-way plots, deviance residuals, and lift charts using statsmodels, CatBoost, and Polars.
A practical comparison of CatBoost and XGBoost for UK personal lines insurance pricing — categorical handling, Tweedie support, and why we default to CatBoost.