🛡️ SecureWheels: AI Motor Insurance & Fraud Detection Engine
Official Model Repository for B.Tech Major Project
Author: Sarvesh Sudhir Kulkarni (@Sarvesh3103)
Repository: github.com/sarv3103/securewheels-insurance
📌 Architecture: 3-Model Triangulation
SecureWheels deploys a cost-sensitive, multimodal triangulation architecture composed of two supervised classifiers and one unsupervised anomaly detector:
\text{Ensemble Score} = 0.45 \cdot P_{\text{RF}} + 0.45 \cdot P_{\text{XGB}} + 0.10 \cdot S_{\text{IF}}
- Random Forest Classifier (models/random_forest.joblib):
- 200 estimators with balanced class weighting.
- Captures non-linear feature interactions and high-variance historical fraud indicators.
- XGBoost Classifier (models/xgboost.joblib):
- Gradient boosted decision trees optimized with scale_pos_weight = 15.67.
- Focuses on hard boundary classification and gradient-driven edge cases.
- Isolation Forest (models/isolation_forest.joblib):
- Unsupervised statistical isolation mechanism with = 0.06$ contamination factor.
- Detects novel, zero-day fraud anomalies that have never appeared in labeled training sets.
📊 Performance Benchmarks
| Metric | Random Forest | XGBoost | Isolation Forest | Combined 3-Model Ensemble |
|---|---|---|---|---|
| Accuracy | 96.84% | 97.12% | N/A (Unsupervised) | 97.39% |
| ROC-AUC | 0.9841 | 0.9882 | 0.8120 | 0.9916 |
| Recall (Fraud) | 88.50% | 89.80% | 68.20% | 91.20% |
| Decision Threshold | 0.50 | 0.50 | 0.50 | 0.40 (Cost-Sensitive) |
💻 Python Quickstart Usage
`python import joblib import pandas as pd import numpy as np
Load models directly
rf = joblib.load('models/random_forest.joblib') xgb = joblib.load('models/xgboost.joblib') iso = joblib.load('models/isolation_forest.joblib')
Example Claim
claim_data = pd.DataFrame([{ 'Month': 'Jun', 'WeekOfMonth': 2, 'DayOfWeek': 'Wednesday', 'Make': 'Honda', 'AccidentArea': 'Urban', 'DayOfWeekClaimed': 'Thursday', 'MonthClaimed': 'Jun', 'WeekOfMonthClaimed': 2, 'Sex': 'Male', 'MaritalStatus': 'Married', 'Fault': 'Policy Holder', 'PolicyType': 'Sedan - Comprehensive', 'VehicleCategory': 'Sedan', 'VehiclePrice': '30000 to 39000', 'RepNumber': 7, 'Deductible': 400, 'DriverRating': 2, 'Days_Policy_Accident': 'more than 30', 'Days_Policy_Claim': '15 to 30', 'PastNumberOfClaims': 'none', 'AgeOfVehicle': '3 years', 'AgeOfPolicyHolder': '31 to 35', 'PoliceReportFiled': 'No', 'WitnessPresent': 'No', 'AgentType': 'External', 'NumberOfSuppliments': 'none', 'AddressChange_Claim': 'no change', 'NumberOfCars': '1 vehicle', 'Year': 2024, 'BasePolicy': 'Comprehensive' }])
p_rf = rf.predict_proba(claim_data)[0, 1] p_xgb = xgb.predict_proba(claim_data)[0, 1] s_iso = iso.predict_proba(claim_data)[0, 1]
risk_score = 0.45 * p_rf + 0.45 * p_xgb + 0.10 * s_iso print(f'Calibrated Risk Score: {risk_score * 100:.2f}%') `