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README.md

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+---
+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.

label_encoders.pkl

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pricing_ebm.pkl

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training_results.json

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+{
+  "glm": {
+    "model": "TweedieGLM",
+    "tweedie_power": 1.5,
+    "mae": 12244.958454220796,
+    "rmse": 17615.02268631013,
+    "mape_pct": 198.8474585306464,
+    "coefficients": {
+      "driver_age": 0.0,
+      "years_driving": 0.0,
+      "years_ncd": 0.0,
+      "vehicle_year": 0.0,
+      "vehicle_value": 0.0,
+      "annual_mileage": 0.0,
+      "voluntary_excess": 0.0,
+      "compulsory_excess": 0.0,
+      "previous_claims_3y": 0.0,
+      "policy_age_days": 0.0,
+      "vehicle_age": 0.0,
+      "driver_experience_ratio": 0.0,
+      "ncd_ratio": 0.0,
+      "vehicle_make_enc": 0.0,
+      "fuel_type_enc": 0.0,
+      "occupation_enc": 0.0,
+      "region_enc": 0.0
+    },
+    "intercept": 9.967596757593236,
+    "n_train": 20000,
+    "n_test": 5000
+  },
+  "ebm": {
+    "model": "EBM",
+    "mae": 11131.778297959956,
+    "rmse": 14787.148537325793,
+    "mape_pct": 177.58336694602855,
+    "n_train": 20000,
+    "n_test": 5000,
+    "top_features": {
+      "previous_claims_3y": 3259.1140028713794,
+      "policy_age_days": 2683.871584881652,
+      "previous_claims_3y & policy_age_days": 1608.1250699587606,
+      "region_enc": 221.31391899112393,
+      "vehicle_make_enc": 173.4298978553976,
+      "voluntary_excess & previous_claims_3y": 172.51716007254487,
+      "annual_mileage": 171.50784229318242,
+      "compulsory_excess": 165.02085743907992,
+      "voluntary_excess": 163.32366251884218,
+      "ncd_ratio": 152.51296273306403
+    }
+  }
+}

tweedie_glm.pkl

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