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 Alto riesgo – requiere revisión manual
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
Escaneo forense dirigido
Un análisis forense multicapa se centra en las regiones identificadas para evaluar la autenticidad y detectar manipulaciones.
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
Región de escaneo
Photo by Freepik
Texto de reclamación: "Mi ventanilla del coche está rota tras un incidente en un estacionamiento."
Consistente con la reclamación
Realismo del patrón de grieta
94%
Ruido en límite de ruptura
88%
Iluminación en fragmentos
91%
Límite de empalme
96%
18 / 100 Puntuación de integridad
AI-generated car, no visible damage
Anomalía
AI-generated image
Texto de reclamación: "Mi ventanilla del coche está rota tras un incidente en un estacionamiento."
Inconsistencias detectadas
Realismo del patrón de grieta
82% anomaly
Ruido en límite de ruptura
78% anomaly
Iluminación en fragmentos
69% anomaly
Límite de empalme
88% anomaly
87 / 100 Puntuación de integridad

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
Alto riesgo – requiere revisión manual

Se detectaron múltiples señales forenses. Se recomienda una revisión adicional.

80-100
Flag for investigation

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

2 de cada 3
minoristas online reportan más fraude
0%
de las devoluciones son fraudulentas
0%
de los consumidores admiten reclamaciones falsas
0%
de las empresas ven el abuso de reembolsos como muy costoso

Estimate your fraud exposure

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

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