data/fraud_simulator_dataset/README.md

Fraud Simulator Dataset

Overview

This dataset contains synthetic insurance claims for fraud detection training and validation.

Dataset Structure

Files

• claims_normal.csv - Legitimate insurance claims
• claims_fraudulent.csv - Fraudulent insurance claims
• claims_combined.csv - Combined dataset with labels
• metadata.json - Dataset metadata and statistics

Schema

Claim Record:

{
  "claim_id": "string",
  "amount": "float",
  "type": "string (auto|property|health|life)",
  "claimant_id": "string",
  "days_since_policy_start": "integer",
  "claimant_history": {
    "claim_count": "integer",
    "avg_amount": "float",
    "total_paid": "float"
  },
  "document_consistency_score": "float (0.0-1.0)",
  "linked_suspicious_entities": "integer",
  "label": "string (fraud|legitimate)"
}

Fraud Patterns Included

1. Staged Accidents: Multiple claims with similar patterns
2. Document Mismatch: Inconsistent documentation
3. Early Claims: Claims filed shortly after policy inception
4. Amount Inflation: Claims significantly above average
5. Entity Networks: Connected suspicious entities
6. High Frequency: Repeated claims from same claimant

Dataset Statistics

• Total Claims: 10,000
• Fraudulent: 2,500 (25%)
• Legitimate: 7,500 (75%)
• Claim Types: Auto (40%), Property (30%), Health (20%), Life (10%)
• Average Claim Amount: $5,000
• Date Range: 2020-2026

Usage

This dataset is used for:

• Model training and validation
• Fraud pattern simulation
• Stress testing
• Drift scenario testing
• Performance benchmarking

Data Quality

• No missing values
• Balanced across claim types
• Realistic fraud patterns based on industry data
• Regular updates with new fraud patterns

Privacy

All data is synthetic and does not contain real PII.

License

For internal use only. Part of BDR-Agent-Factory ecosystem.