Head-to-head evaluations of generic ML tools versus insurance-specific libraries. When a general-purpose library is almost right but breaks on insurance data, these posts explain exactly why — and what to use instead.
A practical comparison of CatBoost and XGBoost for UK personal lines insurance pricing — categorical handling, Tweedie support, and why we default to CatBoost.
sklearn's TweedieRegressor is a well-engineered GLM. It fits a fixed-power Tweedie model correctly. The problem is that insurance pricing needs per-risk variance, not a single p...
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...
MAPIE is the standard Python library for conformal prediction, but it wasn't designed for insurance. Here is what goes wrong with exposure-weighted portfolios and Tweedie models...
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...
EconML is the standard Python library for causal ML. It was not built for insurance pricing, Poisson/Gamma exposure models, or the dual-selection bias problems specific to renew...
DoWhy is the most rigorous general-purpose causal inference library in Python — DAG specification, formal identification, refutation tests. It was not built for insurance pricin...
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...