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10 flagship Python libraries for UK personal lines insurance pricing. Proxy discrimination auditing (FCA Consumer Duty), model validation and governance, DML causal inference, conformal prediction, Wh
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Burning Cost blog and course landing page — open-source insurance pricing tools for UK actuarial and data science teams
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Insurance pricing education and open-source tooling for UK actuaries and pricing teams.
The site at burning-cost.github.io publishes worked examples, methodology explainers, and links to all open-source libraries.
Ten libraries we consider genuinely differentiated — tools for hard problems in UK pricing where no adequate open-source Python solution existed before.Read the full README ↗Insurance pricing education and open-source tooling for UK actuaries and pricing teams.
The site at burning-cost.github.io publishes worked examples, methodology explainers, and links to all open-source libraries.
Ten libraries we consider genuinely differentiated — tools for hard problems in UK pricing where no adequate open-source Python solution existed before.
Regulatory compliance
| Library | Problem it solves |
|---|---|
| insurance-fairness | Proxy discrimination auditing aligned to FCA Consumer Duty and Equality Act 2010 |
| insurance-governance | PRA SS1/23 model validation reports: bootstrap Gini CI, A/E CI, double-lift, renewal cohort test |
Causal inference
| Library | Problem it solves |
|---|---|
| insurance-causal | Double machine learning to deconfound rating factors where channel and behaviour bias standard GLM coefficients |
Uncertainty quantification
| Library | Problem it solves |
|---|---|
| insurance-conformal | Distribution-free prediction intervals with finite-sample coverage guarantees; Solvency II SCR bounds |
| insurance-distributional-glm | GAMLSS for Python: model mean, dispersion, shape, and zero-inflation as functions of covariates |
Smoothing and experience rating
| Library | Problem it solves |
|---|---|
| insurance-whittaker | Whittaker-Henderson 1D/2D smoothing with REML lambda selection and Bayesian credible intervals |
| insurance-credibility | Bühlmann-Straub credibility in Python with mixed-model equivalence — caps thin segments, stabilises NCD factors |
Telematics
| Library | Problem it solves |
|---|---|
| insurance-telematics | HMM driving state classification from raw 1Hz GPS/accelerometer data to GLM-compatible risk scores |
Advanced modelling
| Library | Problem it solves |
|---|---|
| insurance-frequency-severity | Sarmanov copula joint frequency-severity with analytical premium correction — tests the independence assumption every model makes |
| insurance-gam | EBM and Neural Additive Model: transparency of a GLM, predictive power of a neural network |
34 libraries in total, covering the full pricing workflow from data validation through to deployment and monitoring. See burning-cost.github.io/tools for the complete list.