Insurance · Claims Integrity

Claims Integrity

One score that tells your claims team whether to approve, review, or reject an insurance claim, before it costs you money.

Built for insurance carriers and claims processors who need to detect manipulated evidence images at the point of first notice of loss, before the claim enters the pipeline.

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Car with rear bumper damage
AI-generated image
Claims Integrity Score
100 = fully manipulated · 0 = authentic
0 / 100
0-50 50-70 70-80 80-100
Recommended action Escalate to SIU
Context Engine
1 Reading claim
"My car window is broken after an incident in a parking lot."
2 Extracting objects
car window vehicle body parking context
3 Directing analysis
Focusing forensic scan on window region...
4 Region score
Splice boundary: 91% · Noise mismatch: 84% · Texture: 78%

Context-aware detection

Most insurance systems analyze claim images blindly. Our engine reads the claim text first, understands what damage is described, and directs the forensic scan to the exact regions that matter.

01
Claim text parsed
Natural language processing extracts the claimed damage type, affected objects, and circumstances from the FNOL text.
02
Regions identified
The engine maps claimed damage to specific image regions and object boundaries in the submitted evidence photos.
03
Forensic scan directed
Multi-layer forensic analysis focuses on the identified regions: splice detection, noise analysis, lighting consistency, and texture coherence.
04
Score reflects context
The final integrity score weighs forensic signals against the claim narrative, producing a single actionable number for your team.

Real vs. manipulated

"My car window is broken after an incident in a parking lot."

Real car with front collision damage
Scan Region
Photo by Freepik
Claim text: "My car window is broken after an incident in a parking lot."
Consistent with claim
Crack pattern realism
94%
Noise at break boundary
88%
Lighting on shards
91%
Splice boundary
96%
18 / 100 Integrity Score
AI-generated car, no visible damage
Anomaly
AI-generated image
Claim text: "My car window is broken after an incident in a parking lot."
Inconsistencies detected
Crack pattern realism
82% anomaly
Noise at break boundary
78% anomaly
Lighting on shards
69% anomaly
Splice boundary
88% anomaly
87 / 100 Integrity Score

Your rules. Our intelligence.

You define the thresholds. We provide the score. Automate claim decisions or route cases to the right team based on risk level.

0-50
Fast-track approval

Evidence is consistent with the claim. Approve automatically or route to standard processing.

50-70
Request evidence

Some signals are ambiguous. Request additional documentation or photos from the claimant.

70-80
Escalate to SIU

Multiple forensic signals triggered. Route to the Special Investigation Unit for further review.

80-100
Flag for investigation

High confidence of image manipulation. Flag the claim for formal fraud investigation.

0%
Increase in AI-manipulated claims (2023-2025)
$0B
Annual cost of insurance fraud (US)
0%+
Of all claims contain fraud
0%
Of fraudulent claims use manipulated images

Estimate your fraud exposure

Run your claims portfolio through our forensic engine. See what percentage of evidence images contain manipulation.

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