Starting points for UK pricing teams moving to Python, evaluating the Burning Cost toolkit, or rebuilding a workflow from scratch. Covers Python vs R, migrating from Emblem, the full library stack, and end-to-end pipeline guides.
A single freMTPL2 motor pipeline running through insurance-gam, insurance-conformal, insurance-monitoring, insurance-fairness, and insurance-governance. No other open-source eco...
How solvency_capital_range() produces model-free 99.5% SCR bounds, how SCRReport produces the coverage validation table for regulatory submission, and why interval_width is the ...
Migrating from Emblem to Python for insurance GLM pricing: what changes in workflow, what gets easier, what gets harder, and what the transition actually looks like in practice.
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.
A practical walkthrough for pricing analysts: use insurance-causal for causal inference, insurance-conformal for prediction intervals, and insurance-monitoring for drift detecti...
Python library distilling CatBoost GBMs into multiplicative GLM factor tables for Radar and Emblem. Open-source GBM-to-GLM distillation for UK pricing teams.
How to combine GLM and GBM predictions for production pricing: cross-validated blend weights, PRA interpretability, and when blending actually helps. Once the blended model is v...
Bühlmann-Straub vs CatBoost vs two-stage multilevel for UK motor pricing: when each wins and how insurance-credibility and insurance-multilevel combine them.