Visual Document Fraud Detection for Financial Services

import { Mixpeek } from 'mixpeek';

const client = new Mixpeek({ apiKey: process.env.MIXPEEK_API_KEY });

// Analyze document for fraud indicators
async function analyzeDocument(documentUrl: string, documentType: string) {
  const analysis = await client.fraud.analyzeDocument({
    url: documentUrl,
    document_type: documentType,  // 'drivers_license', 'paystub', 'bank_statement'
    checks: [
      'visual_tampering',         // Pixel manipulation, font inconsistencies
      'metadata_analysis',        // EXIF data, creation timestamps
      'template_matching',        // Compare against known genuine templates
      'data_consistency',         // Cross-check extracted data
      'quality_anomalies'         // Unusual image artifacts
    ]
  });

  return {
    risk_score: analysis.risk_score,  // 0-100
    decision: analysis.decision,       // 'approve', 'review', 'reject'
    fraud_indicators: analysis.indicators,
    extracted_data: analysis.extracted_fields,
    confidence: analysis.confidence
  };
}

// Detailed tampering analysis
async function detectTampering(documentId: string) {
  const tampering = await client.fraud.detectTampering({
    document_id: documentId,
    analysis_depth: 'comprehensive',
    checks: [
      'copy_paste_detection',    // Cloned regions
      'font_consistency',        // Mixed fonts indicating edits
      'edge_analysis',           // Unnatural edges from cutting
      'compression_artifacts',   // Re-saved JPEG artifacts
      'lighting_consistency',    // Inconsistent shadows/lighting
      'noise_pattern'            // Sensor noise differences
    ]
  });

  return {
    is_tampered: tampering.is_tampered,
    tampered_regions: tampering.regions.map(r => ({
      bounding_box: r.bbox,
      tampering_type: r.type,
      confidence: r.confidence
    })),
    original_areas: tampering.likely_original,
    manipulation_type: tampering.manipulation_category
  };
}

// Cross-reference data across application documents
async function crossReferenceApplication(applicationId: string) {
  const documents = await getApplicationDocuments(applicationId);

  const crossCheck = await client.fraud.crossReference({
    documents: documents.map(d => ({
      id: d.id,
      type: d.type,
      extracted_data: d.extracted_fields
    })),
    checks: [
      'name_consistency',        // Same name across docs
      'address_consistency',     // Address matches
      'date_consistency',        // Dates make sense
      'income_plausibility',     // Income matches stated employment
      'employer_verification'    // Employer exists, info matches
    ]
  });

  return {
    consistency_score: crossCheck.score,
    discrepancies: crossCheck.discrepancies,
    risk_factors: crossCheck.risk_factors,
    verification_status: crossCheck.overall_status
  };
}

// Real-time fraud decision during application
async function getFraudDecision(applicationId: string) {
  const docAnalysis = await analyzeAllDocuments(applicationId);
  const crossRef = await crossReferenceApplication(applicationId);

  const decision = await client.fraud.getDecision({
    application_id: applicationId,
    document_scores: docAnalysis,
    cross_reference: crossRef,
    policy: 'standard_underwriting',
    thresholds: {
      auto_approve: 20,    // Risk score below 20
      auto_reject: 80,     // Risk score above 80
      manual_review: 'between'
    }
  });

  return {
    decision: decision.action,
    risk_score: decision.composite_score,
    primary_factors: decision.top_risk_factors,
    requires_stepup: decision.step_up_verification,
    processing_time_ms: decision.latency
  };
}