# 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:**
```json
{
  "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.