Head-to-head evaluations. Generic ML libraries versus insurance-specific tools, Python versus R for specific tasks, different modelling approaches on the same dataset. Conclusions backed by benchmark results. 7 articles.
When you have fewer than 5,000 policies in a segment, should you use Bühlmann-Straub credibility or a GBM with transfer learning? The answer depends on whether you have a relate...
Conformal prediction and the parametric bootstrap both produce prediction intervals for insurance pricing models. They answer different questions, have different computational c...
Two approaches to prediction intervals for insurance severity: distributional GAMLSS (insurance-distributional-glm) vs distribution-free conformal (insurance-conformal). Same sy...
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...
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...