InsurePricing

InsurePricing — Insurance Premium Pricing Models

Created by 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:

New to These Hugging Face Repos?

INSUREOS is published as separate repos so each page stays focused:

• This page (InsurePricing) contains model artifacts and usage.
• Full training code is in piyushptiwari/insureos-models.
• Training data is in piyushptiwari/insureos-training-data.
• Beginner explanation is in LEARN.md.

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

Risk Factors (17 Features)

EBM Top Feature Importances

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

How to Use

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)

Learn How This Model Works

New to ML or insurance AI? We wrote a plain-English guide that explains the concepts, the data, and the actual training code behind every INSUREOS model:

• Learning guide: LEARN.md
• Training code: piyushptiwari/insureos-models — this model is trained by training/pricing_glm.py

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:

Citation

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

About Bytical AI

Bytical 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.