Automating Claims Processing with Machine Learning

Traditional insurance claims take weeks to process. At CoverKit, we use machine learning to auto-approve 90% of claims within 24 hours while maintaining industry-leading accuracy and fraud prevention.

The Problem with Traditional Claims

Insurance claims have historically been slow and frustrating. A typical shipping protection claim involves:

• Filing paperwork (often by mail or phone)
• Waiting for acknowledgment (3-5 business days)
• Submitting documentation (photos, receipts, etc.)
• Manual review by claims adjusters (1-2 weeks)
• Payment processing (another 1-2 weeks)

Total time: 3-6 weeks for what is often a $50-200 claim. This experience does not match modern customer expectations.

Our ML-Powered Approach

We built an automated claims processing pipeline that evaluates claims in real-time:

How It Works

// Simplified claim processing pipeline
async function processClaim(claim: Claim): Promise<ClaimDecision> {
  // Step 1: Extract features
  const features = await extractFeatures(claim);

  // Step 2: Run fraud detection
  const fraudScore = await fraudModel.predict(features);

  if (fraudScore > FRAUD_THRESHOLD) {
    return { decision: 'manual_review', reason: 'fraud_flag' };
  }

  // Step 3: Run claim analysis
  const claimScore = await claimModel.predict(features);

  // Step 4: Apply business rules
  const rules = await evaluateRules(claim, claimScore);

  if (claimScore > AUTO_APPROVE_THRESHOLD && rules.passed) {
    // Auto-approve
    await initiatePayment(claim);
    return { decision: 'approved', payoutTime: '24h' };
  }

  if (claimScore < AUTO_DENY_THRESHOLD) {
    return { decision: 'denied', reason: rules.denialReason };
  }

  // Edge cases go to human review
  return { decision: 'manual_review', reason: 'model_uncertain' };
}

Feature Engineering

The models consider hundreds of features across several categories:

Claim Features

• Claim amount relative to policy coverage
• Time between purchase and claim
• Claim type (loss, damage, theft)
• Description sentiment and completeness

Customer Features

• Previous claim history
• Account age and verification status
• Geographic patterns
• Device and behavioral signals

Contextual Features

• Carrier and route risk scores
• Seasonal patterns
• Market-wide claim trends
• Weather and external events

Results

Human-in-the-Loop

While automation handles the majority of claims, we maintain human oversight:

• Edge Cases: Claims where the model is uncertain get human review
• Appeals: Customers can always request human review of any decision
• Model Training: Human decisions are fed back to improve models
• Auditing: Regular review of automated decisions for fairness and accuracy

What is Next

We are continuously improving our claims automation:

• Computer vision for damage photo analysis
• Natural language understanding for claim descriptions
• Real-time carrier data integration
• Predictive claims (identifying likely claims before they happen)

Try It Yourself

Experience our automated claims processing by signing up for CoverKit. Our sandbox environment lets you simulate claims and see the automation in action.

Have questions about our ML infrastructure? Reach out to our team.