InsureFraudNet — Insurance Fraud Detection

Created by Bytical AI — AI agents that run insurance operations.

Model Description

InsureFraudNet is a multi-line-of-business fraud detection system for UK insurance claims. It consists of paired XGBoost classifiers and Isolation Forest anomaly detectors for three lines of business: Motor, Property, and Liability.

Architecture

Each line of business has:

• XGBoost Classifier — Supervised gradient-boosted tree for fraud probability scoring
• Isolation Forest — Unsupervised anomaly detection for novel fraud patterns

Lines of Business

LoB	Training Claims	Fraud Rate	Features	AUC-ROC	F1
Motor	25,000	8%	23	1.000	1.000
Property	15,000	8%	20	1.000	1.000
Liability	10,000	8%	14	1.000	1.000

Top Fraud Indicators by LoB

Motor:

Feature	Importance
claim_reserve_ratio	48.9%
days_to_report	43.7%
policy_age_days	5.7%
previous_claims_3y	1.4%

Property:

Feature	Importance
days_to_report	40.9%
policy_age_days	37.6%
claim_reserve_ratio	20.0%
previous_claims_3y	1.4%

Liability:

Feature	Importance
previous_claims_3y	56.1%
days_to_report	43.9%

Files

File	Description
xgb_motor.json	XGBoost model for motor fraud
xgb_property.json	XGBoost model for property fraud
xgb_liability.json	XGBoost model for liability fraud
iforest_motor.pkl	Isolation Forest for motor anomalies
iforest_property.pkl	Isolation Forest for property anomalies
iforest_liability.pkl	Isolation Forest for liability anomalies
training_results.json	Full training metrics and feature importance

How to Use

import xgboost as xgb
import pickle
import numpy as np

# Load motor fraud model
model = xgb.XGBClassifier()
model.load_model("xgb_motor.json")

# Load isolation forest
with open("iforest_motor.pkl", "rb") as f:
    iforest = pickle.load(f)

# Example claim features
claim = np.array([[
    35,     # driver_age
    10,     # years_driving
    5,      # years_ncd
    2020,   # vehicle_year
    25000,  # vehicle_value
    12000,  # annual_mileage
    800,    # premium
    250,    # voluntary_excess
    100,    # compulsory_excess
    5000,   # reserve_amount
    4500,   # claim_amount
    0,      # recovery_amount
    0,      # previous_claims_3y
    3,      # days_to_report
    365,    # policy_age_days
    1,      # witnesses
    1,      # dashcam
    1,      # police_report
    0.9,    # claim_reserve_ratio
    5.625,  # claim_premium_ratio
    0,      # new_policy
    0,      # late_report
    4       # vehicle_age
]])

# Predict fraud probability
fraud_prob = model.predict_proba(claim)[0][1]
is_anomaly = iforest.predict(claim)[0] == -1

print(f"Fraud probability: {fraud_prob:.2%}")
print(f"Anomaly detected: {is_anomaly}")

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	Insurance domain LLM	ROUGE-1: 0.384
InsureDocClassifier	12-class document classification	F1: 1.0
InsureNER	13-entity Named Entity Recognition	F1: 1.0
InsureFraudNet (this model)	Fraud detection (Motor/Property/Liability)	AUC-ROC: 1.0
InsurePricing	Insurance pricing (GLM + EBM)	MAE: £11,132

Citation

@misc{bytical2026insurefraudnet,
  title={InsureFraudNet: Multi-LoB Insurance Fraud Detection},
  author={Bytical AI},
  year={2026},
  url={https://huggingface.co/piyushptiwari/InsureFraudNet}
}

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.