---
language:
- en
license: apache-2.0
tags:
- insurance
- pricing
- glm
- ebm
- explainable-ai
- uk-insurance
- tabular-regression
- actuarial
- bytical
pipeline_tag: tabular-regression
datasets:
- piyushptiwari/insureos-training-data
model-index:
- name: InsurePricing
  results:
  - task:
      type: tabular-regression
      name: Insurance Premium Pricing
    metrics:
    - type: mae
      value: 11132
      name: MAE — EBM (£)
    - type: mae
      value: 12245
      name: MAE — GLM (£)
---

# InsurePricing — Insurance Premium Pricing Models

**Created by [Bytical AI](https://bytical.ai)** — AI agents that run insurance operations.

## Model Description

InsurePricing provides two complementary pricing models for UK motor insurance premiums, designed for actuarial and underwriting workflows:

1. **Tweedie GLM** — Generalized Linear Model with Tweedie distribution (power=1.5), the industry-standard approach for insurance pricing
2. **Explainable Boosting Machine (EBM)** — Interpretable glass-box model from Microsoft Research (InterpretML) that provides per-feature explanations

### Model Comparison

| Model | MAE (£) | RMSE (£) | MAPE (%) | Interpretable |
|-------|---------|----------|----------|---------------|
| **EBM** | **£11,132** | £14,787 | 177.6% | Yes — per-feature shape functions |
| **Tweedie GLM** | £12,245 | £17,615 | 198.8% | Yes — coefficients |

### Risk Factors (17 Features)

| Feature | Type | Description |
|---------|------|-------------|
| driver_age | Numeric | Age of primary driver |
| years_driving | Numeric | Years of driving experience |
| years_ncd | Numeric | No-claims discount years |
| vehicle_year | Numeric | Year of vehicle manufacture |
| vehicle_value | Numeric | Vehicle market value (£) |
| annual_mileage | Numeric | Estimated annual miles |
| voluntary_excess | Numeric | Voluntary excess amount (£) |
| compulsory_excess | Numeric | Compulsory excess amount (£) |
| previous_claims_3y | Numeric | Claims in last 3 years |
| policy_age_days | Numeric | Days since policy inception |
| vehicle_age | Derived | Current year minus vehicle_year |
| driver_experience_ratio | Derived | years_driving / driver_age |
| ncd_ratio | Derived | years_ncd / years_driving |
| vehicle_make_enc | Encoded | Vehicle manufacturer |
| fuel_type_enc | Encoded | Fuel type |
| occupation_enc | Encoded | Driver occupation |
| region_enc | Encoded | UK region |

### EBM Top Feature Importances

| Feature | Importance |
|---------|-----------|
| previous_claims_3y | 3,259 |
| policy_age_days | 2,684 |
| previous_claims_3y × policy_age_days | 1,608 |
| region_enc | 221 |
| vehicle_make_enc | 173 |
| annual_mileage | 172 |
| compulsory_excess | 165 |
| voluntary_excess | 163 |
| ncd_ratio | 153 |

### Training Data

- 25,000 synthetic UK motor insurance policies (20K train / 5K test)
- Features include driver demographics, vehicle attributes, claim history, and policy details

### Files

| File | Description |
|------|-------------|
| `tweedie_glm.pkl` | Scikit-learn Tweedie GLM pipeline |
| `pricing_ebm.pkl` | InterpretML EBM model |
| `label_encoders.pkl` | Fitted label encoders for categorical features |
| `training_results.json` | Full training metrics and feature coefficients |

## How to Use

```python
import pickle
import numpy as np

# Load EBM model
with open("pricing_ebm.pkl", "rb") as f:
    ebm = pickle.load(f)
with open("label_encoders.pkl", "rb") as f:
    encoders = pickle.load(f)

# Example: price a motor policy
features = np.array([[
    30,     # driver_age
    8,      # years_driving
    4,      # years_ncd
    2022,   # vehicle_year
    20000,  # vehicle_value
    10000,  # annual_mileage
    200,    # voluntary_excess
    100,    # compulsory_excess
    0,      # previous_claims_3y
    180,    # policy_age_days
    4,      # vehicle_age
    0.267,  # driver_experience_ratio
    0.5,    # ncd_ratio
    3,      # vehicle_make_enc
    1,      # fuel_type_enc
    5,      # occupation_enc
    7       # region_enc
]])

premium = ebm.predict(features)[0]
print(f"Predicted premium: £{premium:,.2f}")

# Get per-feature explanations (EBM glass-box)
explanations = ebm.explain_local(features)
```

## Part of the INSUREOS Model Suite

This model is part of the **INSUREOS** — a complete AI/ML suite for insurance operations built by Bytical AI:

| Model | Task | Metric |
|-------|------|--------|
| [InsureLLM-4B](https://huggingface.co/piyushptiwari/InsureLLM-4B) | Insurance domain LLM | ROUGE-1: 0.384 |
| [InsureDocClassifier](https://huggingface.co/piyushptiwari/InsureDocClassifier) | 12-class document classification | F1: 1.0 |
| [InsureNER](https://huggingface.co/piyushptiwari/InsureNER) | 13-entity Named Entity Recognition | F1: 1.0 |
| [InsureFraudNet](https://huggingface.co/piyushptiwari/InsureFraudNet) | Fraud detection (Motor/Property/Liability) | AUC-ROC: 1.0 |
| **InsurePricing** (this model) | Insurance pricing (GLM + EBM) | MAE: £11,132 |

## Citation

```bibtex
@misc{bytical2026insurepricing,
  title={InsurePricing: Explainable Insurance Premium Pricing Models},
  author={Bytical AI},
  year={2026},
  url={https://huggingface.co/piyushptiwari/InsurePricing}
}
```

## About Bytical AI

[Bytical](https://bytical.ai) builds AI agents that run insurance operations — claims automation, underwriting intelligence, digital sales, and core system modernization for insurers across the UK and Europe. Microsoft AI Partner | NVIDIA | Salesforce.