Python-specific tooling, workflows, and ecosystem coverage for insurance pricing teams. Package selection, Databricks integration, and the practical matters of running Python in a regulated pricing environment. 8 articles.
A complete Python tutorial for building a Tweedie GLM for insurance pricing: synthetic motor data, statsmodels, exposure offset, interpreting the p parameter, residual diagnosti...
Richman-Wüthrich's one-shot PtU reserving paper (arXiv:2603.11660) ships with R code only. We map the algorithm to Python, explain the censored-claims exposure mechanism that ma...
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.
Build a burning cost model in Python: frequency-severity split, exposure offsets, large loss capping, IBNR adjustment, and combined pure premium for UK pricing.
Insurance walk-forward cross-validation prevents the look-ahead bias that makes standard k-fold results useless for prospective evaluation. Complete Python example with insuranc...