Screening auto claims photos at scale: a practical approach

Auto insurance claims represent the highest volume of visual evidence in the insurance industry. Each claim typically includes 8-12 photos of vehicle damage, and carriers process millions of claims annually.

The manipulation problem

The National Insurance Crime Bureau (NICB) tracks and combats insurance fraud across the industry. Common photo fraud patterns in auto claims include:

• Damage inflation: Using AI or editing tools to enhance or add damage to vehicle photos.
• Pre-existing damage: Submitting photos of old damage with altered metadata.
• Staged accidents: Fabricated evidence of accidents that never occurred.
• Photo recycling: Submitting the same damage photos across multiple claims or carriers.

Requirements for detection at scale

Processing millions of claims requires detection that meets specific criteria:

• Sub-second processing: Each photo must be analyzed without noticeable delay.
• High accuracy: Both false positives and false negatives have real costs.
• Seamless integration: Detection must work with existing photo capture apps and claims platforms.

Implementation approach

A three-stage workflow:

1. Intake screening: Every photo submitted with a claim is automatically analyzed.
2. Risk scoring: Claims are flagged based on detection results, metadata analysis, and other signals.
3. Smart routing: High-risk claims go to SIU with detection evidence attached. Low-risk claims proceed to expedited processing.

This approach improves SIU efficiency by focusing investigator time on claims with objective evidence, while allowing legitimate claims to move faster.

Reality AI processes auto claims photos in under one second per image. For a typical 10-photo claim, authentication completes in under 10 seconds, adding negligible time to intake.

The Casualty Actuarial Society (CAS) publishes research on claims analytics, and the actuarial case for automated screening is straightforward: the cost of screening every photo is far less than the cost of undetected fraud.