Auto Insurance Claim Triage with Fraud Detection

Advanced machine learning fraud detection system combining XGBoost and CNN ResNet50 for multi-modal insurance claim analysis.

Features

Multi-Modal Machine Learning

• XGBoost: Tabular data analysis with SHAP explanations
• ResNet50: Computer vision for claim images
• Ensemble Learning: Weighted voting and confidence calibration

Interactive Dashboard

• Real-time fraud detection
• Batch claim processing
• Model explainability (SHAP values)
• Performance analytics
• Risk level visualization

Advanced Capabilities

• Uncertainty quantification
• Confidence intervals
• Feature importance analysis
• Cross-validation metrics
• Hyperparameter optimization

Technical Details

Models

• Tabular: XGBoost with frequency encoding and SHAP
• Image: ResNet50 with transfer learning and Grad-CAM
• Ensemble: Multiple fusion strategies (weighted, conservative, confidence-based)

Performance

• Accuracy: 89%+ on test data
• Latency: <2s per prediction
• Scalability: Batch processing up to 1000 claims

Usage Examples

Single Claim Analysis

1. Upload claim image
2. Enter incident description
3. Specify claim amount and type
4. Get instant fraud risk assessment

Batch Processing

1. Upload CSV with multiple claims
2. Process all claims simultaneously
3. Download comprehensive results

Model Insights

• View SHAP explanations
• Analyze feature importance
• Monitor model performance
• Calibrate risk thresholds