Intelligent Automotive Insurance Fraud Detection
Insurance fraud costs the EU over €20 billion annually. In the automotive sector, workshop invoice fraud is particularly prevalent—ranging from inflated parts prices to "phantom work" (charging for services never performed). Current fraud detection relies on manual claim adjusters, which is time-consuming, error-prone, and expensive.
FraudLens is an intelligent API that automatically analyzes workshop invoices and damage photos. Using AWS Textract for document extraction and AWS Bedrock for AI analysis, it detects anomalies across multiple layers: parts pricing irregularities, phantom work patterns, vehicle history inconsistencies, and repair scope mismatches. The system assigns risk scores to each claim, enabling faster and more accurate fraud detection.
Invoice Upload → AWS Textract OCR → Data Extraction → AWS Bedrock Analysis → Multi-Layer Validation → Risk Scoring
AWS Textract extracts all relevant data from workshop invoices: parts, labor hours, pricing, shop name, vehicle info. Handles various invoice formats and document quality levels.
Detects overpriced parts by comparing against market databases. Identifies suspiciously expensive items for specific vehicle types and models.
AI-powered analysis identifies work that doesn't match damage photos or vehicle condition. Flags inconsistencies between repair scope and actual damage.
Validates repairs against vehicle maintenance history. Detects impossible repairs (e.g., engine replacement for minor fender bender).
Load-tested infrastructure handles enterprise volume. Processes hundreds of claims simultaneously without degradation or timeout issues.
Outputs comprehensive risk score (0-100%) for each claim. Enables triage: high-risk claims for manual review, low-risk for auto-approval.
Compares quoted parts prices against industry databases. Flags unusually expensive items, aftermarket overcharges, and non-OEM substitutions without disclosure.
Validates labor hours against standard repair times for specific vehicles and damage types. Detects inflated labor rates or suspicious time estimates.
AI-powered comparison between damage photos and repair list. Identifies work that shouldn't exist based on visible damage extent.
Cross-references claim repairs against vehicle maintenance history. Detects impossible repairs or patterns suggesting systematic fraud.
Machine learning model analyzes claims against historical data. Identifies outliers and fraud patterns specific to regions, shops, and vehicle types.
500+ concurrent requests
Invoice upload to risk score
Against known fraud patterns
Multi-modal validation
Working with AWS Textract revealed importance of document quality, preprocessing, and handling edge cases (damaged documents, unusual formats, multiple languages).
Load testing with 500+ concurrent requests exposed database connection pooling issues. Implemented optimization strategies that reduced response times by 60%.
Combining multiple AI models (Textract + Bedrock) required careful orchestration and fallback strategies. Learned importance of prompt engineering for consistent fraud detection.
Handling sensitive insurance and vehicle data required GDPR-compliant processing. Implemented encryption, data retention policies, and audit logging.
Target Market: German insurance SMEs (mid-size insurers with €100M-€1B annual claims). Currently underserved by expensive enterprise solutions from major providers.
Value Proposition: FraudLens reduces claim processing time by 40%, improves fraud detection by 3x, and costs 10x less than enterprise competitors. ROI typically achieved within 6 months.
Why This Project: FraudLens demonstrates ability to take complex business problems and deliver scalable, AI-powered solutions. The pivoting from FaceAuth to FraudLens shows pragmatism and market understanding—choosing projects with clearer product-market fit.
Try uploading a test invoice and damage photo to see multi-layer fraud detection in real-time.