insurance

Claims processing: speeding up insurance workflows with AI

How to benchmark AI models for insurance claims extraction, balancing speed, accuracy, and fraud detection.

9 min read

Insurance claims processing sits at the intersection of two competing pressures: customers demand fast payouts, while insurers need thorough documentation review.

The tension is real. Slow processing means unhappy customers and higher operational costs. But rushing means missed fraud indicators and overpayments. Finding the right AI model means optimizing for both.

The claims extraction challenge

Insurance claims involve multiple document types:

Claims document types
Document typeKey fieldsComplexity
Claim forms Policy number, incident date, description Medium
Medical reports Diagnosis, treatment, prognosis High
Police reports Incident details, parties involved Medium
Invoices/receipts Amounts, dates, vendors Low
Photos Damage assessment, verification High

Example scenario

Sample input

An auto insurance claim package containing:

Sample output

{
  "claim": {
    "number": "CLM-2024-789456",
    "policy_number": "AUTO-123456789",
    "date_filed": "2024-03-18"
  },
  "incident": {
    "date": "2024-03-15",
    "time": "14:30",
    "location": "Intersection of Main St and Oak Ave",
    "description": "Rear-end collision at traffic light"
  },
  "damage": {
    "severity": "Moderate",
    "affected_areas": ["Rear bumper", "Trunk lid", "Tail lights"],
    "driveable": true
  },
  "repair_estimate": {
    "labor": 1250.00,
    "parts": 2340.00,
    "paint": 680.00,
    "total": 4270.00,
    "shop": "Premier Auto Body"
  },
  "third_party": {
    "involved": true,
    "at_fault": false,
    "other_insurance": "StateFarm Policy #SF-987654"
  }
}

Model comparison

Model comparison
5 models
# ModelAccuracyCostTime
1 GPT-4o 94.2% $0.031 2.8s
2 Gemini 2.0 Flash 91.6% $0.003 1.2s
3 GPT-4o-mini 89.4% $0.004 1.4s
4 Claude 3.5 Haiku 87.8% $0.010 1.0s
5 Gemini 1.5 Flash 86.2% $0.002 0.9s
Best accuracy 94.2%
Lowest cost $0.002
Fastest 0.9s

The fraud detection dimension

Insurance claims extraction isn’t just about data accuracy—it’s about identifying fraud indicators. Models differ significantly in their ability to flag suspicious patterns:

Fraud detection
Recall vs FP
# ModelFraud indicator recallFalse positive rate
1 GPT-4o 84.6% 6.8%
2 Gemini 2.0 Flash 78.2% 8.4%
3 GPT-4o-mini 72.4% 11.2%

Missing a fraud indicator costs more than a false positive. High recall on fraud detection is worth the trade-off of slightly more manual reviews.

Cost-accuracy sweet spot

For high-volume claims processing, finding models that meet both accuracy and cost thresholds is critical:

Cost-accuracy zones
Sweet spots
Target zoneModels meeting criteria
>92% accuracy, <$0.035/doc GPT-4o
>88% accuracy, <$0.005/doc Gemini 2.0 Flash, GPT-4o-mini

Transformation metrics

What does AI-powered claims processing deliver?

Transformation metrics
Impact
MetricBeforeAfterChange
Processing time 14.2 days 2.1 days -85%
Cost per claim $42 $18 -57%
Fraud detection rate 78% 94% +16 pts

The improved fraud detection alone can save millions annually.

Key insights for insurance claims

1. Fraud detection recall matters more than precision

Missing a fraudulent claim is more expensive than investigating a false positive. Optimize for recall on fraud indicators.

2. Multi-document claims need consistent extraction

A single claim may have 5-10 documents. Consistency across document types matters as much as accuracy on individual documents.

3. Processing speed drives customer satisfaction

Faster claims resolution directly impacts customer retention and Net Promoter Score.

4. Volume justifies premium models

At 100,000+ claims annually, even small accuracy improvements justify higher per-document costs.

Try it yourself

LLMCompare helps insurance teams find the right balance between speed, accuracy, and fraud detection. Upload your claims documents, define your extraction schema, and get the data you need for confident model selection.

Because in insurance, the right model pays for itself.