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 hands-on Python tutorial for insurance pricing analysts on survival analysis and lapse modelling. Covers Kaplan-Meier, Weibull AFT, mixture cure models, customer lifetime valu...
How to set up insurance model monitoring in Python from scratch: PSI, Gini drift, and A/E with the insurance-monitoring library. Know when to redeploy, recalibrate, or refit.
End-to-end GLM frequency model in Python using freMTPL2 from OpenML. Data prep, exposure handling, glum fitting, deviance residuals, actual vs expected, and factor relativity ex...
A practical Python tutorial on credibility theory for insurance pricing analysts. Covers Buhlmann-Straub model, the insurance-credibility library, UK motor example, GLM integrat...
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...