Open source · UK insurance pricing

Your GBM outperforms.
Your GLM is still live.

14 Python libraries that bridge the gap: from walk-forward cross-validation to constrained rate optimisation. Written for teams that already know what a GLM is.

Training course → Browse on GitHub
shap_relativities_demo.py
14 Libraries
600+ Tests
17 Articles
8 Course modules
The problem we solve

The missing piece is not technical skill. It is tooling that bridges the two.

Most UK pricing teams have adopted GBMs but are still taking GLM outputs to production. The GBM sits on a server outperforming the production model, but the outputs are not in a form that a rating engine, regulator, or pricing committee can work with. The model never makes it to rates.

Each library here solves one specific problem in the pricing workflow. Actuarial tests are included. Outputs use the formats pricing teams already recognise: factor tables, Lorenz curves, A/E ratios, movement-capped rate changes.

sklearn-compatible where it matters. Documented by people who have sat in the same sign-off meetings you have.

See it in practice

Three lines to a factor table. Five to validated splits.

Real API calls from the libraries. Not wrappers around wrappers. Each one does the specific thing a pricing team needs.

from shap_relativities import SHAPRelativities sr = SHAPRelativities(model, X_train) factors = sr.fit_transform(X_test) # Returns multiplicative factor tables in GLM format # Same structure as exp(beta) from your Emblem model factors.head() # vehicle_age relativity ci_lower ci_upper # 0 1.000 0.982 1.018 # 1 0.912 0.901 0.923 print(f"Reconstruction R² = {sr.reconstruction_r2:.4f}") # Reconstruction R² = 0.9973
Factor tables, confidence intervals, exposure weighting, reconstruction validation. Output goes straight into a pricing committee pack.
from insurance_cv import InsuranceTemporalCV from sklearn.model_selection import cross_val_score cv = InsuranceTemporalCV( n_splits=5, ibnr_buffer_months=6 ) scores = cross_val_score( model, X, y, cv=cv, scoring="poisson_deviance" ) # Walk-forward splits - no future data leaks into training folds # IBNR buffer prevents immature periods contaminating validation print(f"CV deviance: {scores.mean():.4f} ± {scores.std():.4f}")
Walk-forward splits with configurable IBNR buffers. Temporally correct: no future data leaks into training folds. sklearn-compatible API.
from rate_optimiser import RateOptimiser opt = RateOptimiser( current_rates, technical_rates, exposure ) result = opt.optimise( max_movement=0.10, target_lr_improvement=0.03 ) # Efficient frontier as a linear programme # Respects ±10% movement cap per segment print(f"LR improvement: {result.lr_delta:.1%}") # LR improvement: 2.8% (within movement constraints)
Formulates the efficient frontier as a linear programme. Respects movement caps per segment, targets aggregate loss ratio improvement.
Who this is for

Built for people who know the problem from the inside

These libraries assume you understand insurance pricing. They do not explain what a GLM is.

PA
Pricing actuaries moving from Emblem or Radar to Python

You know the techniques. These libraries give you Python equivalents that produce outputs in the same formats you already use: factor tables, A/E ratios, Lorenz curves.

DS
Data scientists joining an insurance pricing team

You have the ML skills but lack the actuarial context. These libraries encode that context: correct cross-validation for IBNR, credibility-weighted factors, fairness tests that map to FCA requirements.

PM
Pricing managers evaluating modern tooling

You need to know what is production-ready and what is a research prototype. Each library here has actuarial tests, a clear scope, and outputs a pricing team lead can explain to a committee.

AR
Academic researchers working on insurance pricing methods

We implement recent literature: Manna et al. (2025) on conformal prediction, BYM2 spatial models, variance-weighted non-conformity scores. Reproducible, documented, testable.

Open-source libraries

The full pricing workflow, covered

Each library solves one well-defined problem. Actuarial tests included. sklearn-compatible where it matters.

📉
Data & Features
insurance-cv
→
🧠
Model Fitting
credibility bayesian-pricing
→
🔍
Interpretation
shap-relativities ins-interactions insurance-distill
→
✓
Validation
ins-conformal ins-monitoring
→
⚖
Compliance
ins-fairness
→
📈
Rates & Commercial
rate-optimiser ins-demand experience-rating
🔍 Model interpretation
shap-relativities Extract multiplicative rating factor tables from CatBoost models using SHAP values. Same output format as exp(β) from a GLM: factor tables, confidence intervals, exposure weighting, reconstruction validation. SHAP CatBoost factor tables insurance-distill GBM-to-GLM distillation for insurance pricing. Fit a GLM that approximates a GBM's predictions, giving you a transparent, auditable model that regulators and rating engines can work with. model distillation GLM GBM
✓ Validation
insurance-cv Temporally-correct cross-validation for insurance pricing models. Walk-forward splits with configurable IBNR buffers, Poisson and Gamma deviance scorers, sklearn-compatible API. walk-forward sklearn IBNR insurance-conformal Distribution-free prediction intervals for insurance GBMs. Implements the variance-weighted non-conformity score from Manna et al. (2025), producing 30% narrower intervals than the naive approach with identical coverage guarantees. conformal prediction GBM insurance-monitoring Three-layer model monitoring for deployed pricing models. Exposure-weighted PSI/CSI, segmented A/E ratios with IBNR adjustment, and a formal Gini z-test to distinguish recalibration from refit signals. PSI/CSI A/E ratios Gini z-test
⚙ Techniques
credibility Buhlmann-Straub credibility in Python, with mixed-model equivalence checks. Practical for capping thin segments, stabilising NCD factors, and blending a new model with an incumbent rate. Buhlmann-Straub Polars bayesian-pricing Hierarchical Bayesian models for thin-data pricing segments. Partial pooling across risk groups, with credibility factor output in a format that maps back to traditional actuarial review. hierarchical Bayes partial pooling insurance-interactions Tools for detecting, quantifying, and presenting interaction effects in insurance pricing models: the effects a main-effects-only GLM cannot see. interaction effects GLM diagnostics insurance-causal Causal inference methods for insurance pricing. Separating genuine risk signal from confounded association, relevant wherever rating factors are correlated with distribution channel or policyholder behaviour. causal inference deconfounding insurance-spatial Spatial territory ratemaking using BYM2 models. Geographically smoothed relativities that borrow strength across adjacent areas, particularly useful for postcode-level home and motor models with thin data. BYM2 spatial smoothing postcode
📈 Commercial
rate-optimiser Constrained rate change optimisation for UK personal lines. Formulates the efficient frontier between loss ratio improvement and movement cap constraints as a linear programme. linear programming movement caps insurance-demand Demand and conversion modelling for insurance pricing. Price elasticity curves, own-price and cross-price effects, integration with rate optimisation. price elasticity conversion experience-rating NCD/bonus-malus systems, experience modification factors, and schedule rating for UK insurance pricing. Principled adjustment of technical rates based on individual risk history. NCD bonus-malus schedule rating
⚖ Compliance
insurance-fairness Proxy discrimination detection for insurance pricing models. Measures of disparate impact, fairness-accuracy trade-off analysis, FCA Consumer Duty documentation support. FCA Consumer Duty proxy discrimination
Training course

Modern Insurance Pricing with Python and Databricks

Eight modules written for pricing actuaries and analysts at UK personal lines insurers. Every module covers a real pricing problem, not a generic data science tutorial adapted to insurance. You work through real Databricks notebooks, on synthetic data that behaves like the real thing.

Full course + all tools
£295
All 8 modules · all future updates · all Burning Cost tools
One-time payment — no subscription
See the full course →
  • 01 Databricks for pricing teams
  • 02 GLMs in Python: the bridge from Emblem
  • 03 GBMs for insurance pricing
  • 04 SHAP relativities
  • 05 Conformal prediction intervals
  • 06 Credibility and Bayesian pricing
  • 07 Constrained rate optimisation
  • 08 End-to-end pipeline capstone
From the blog

Practitioner articles on insurance pricing

07 Mar 2026
Your Rating Factor Might Be Confounded
When exp(beta) from a GLM is not what you think it is. How omitted variable bias and confounding distort rating factor estimates, and how Double Machine Learning produces cleaner causal estimates.
Read article →
07 Mar 2026
Your Pricing Model Might Be Discriminating
How to detect and correct proxy discrimination in UK insurance pricing models. Using SHAP and the insurance-fairness library to identify protected characteristic leakage under FCA Consumer Duty.
Read article →
07 Mar 2026
Your Pricing Model is Drifting (and You Probably Can't Tell)
PSI and aggregate A/E are not enough. A three-layer monitoring framework - feature drift, segmented calibration, and a formal Gini test - that tells you whether to recalibrate or refit.
Read article →
07 Mar 2026
Your Demand Model Is Confounded
Naive price elasticity estimates from insurance quote data are biased - risk drives both premium and lapse. The insurance-demand library implements Double Machine Learning to fix this, covering the full conversion, retention, elasticity, and demand curve pipeline with FCA GIPP compliance built in.
Read article →
07 Mar 2026
From CatBoost to Radar in 50 Lines of Python
An open-source Python library that distils GBM models into multiplicative GLM factor tables for Radar, Emblem, and other rating engines. The first open-source solution for the most common deployment problem in UK pricing.
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07 Mar 2026
Your NCD Threshold Advice Is Wrong at 65%
A Python library for NCD/bonus-malus systems, experience modification factors, and schedule rating. Includes the non-obvious finding that optimal NCD claiming thresholds peak at 30% NCD, not 65%.
Read article →
07 Mar 2026
Demand Modelling for Insurance Pricing
How to build a demand model for UK personal lines pricing: conversion, retention, price elasticity, and demand curves. Covers FCA GIPP requirements and the tools that make it tractable.
Read article →
07 Mar 2026
How Much of Your GLM Coefficient Is Actually Causal?
GLM coefficients measure association, not causation. How Double Machine Learning isolates the causal effect of rating factors from confounding, and why this matters for FCA-compliant pricing.
Read article →
06 Mar 2026
Why Your Cross-Validation is Lying to You
Standard k-fold cross-validation is wrong for insurance pricing models. How temporal leakage and IBNR contamination inflate CV scores, and how walk-forward validation fixes both problems.
Read article →
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