Satellite-Based Insurance Claim Verification
Built a geospatial AI claim validation workflow that compares pre/post-disaster imagery, accelerates claims handling, and raises fraud detection accuracy.
+50%
Processing Speed
+35%
Fraud Detection
Reduced
Manual Surveys
Faster
Disaster Response
Challenge to Solution
How we translated complex operational constraints into a clear implementation path.
The Challenge
Manual surveys delayed claims decisions during high-volume disaster events
Fraudulent damage claims were difficult to isolate quickly at scale
Field access constraints increased risk and operational cost for survey teams
Adjusters lacked standardized evidence across geographies and claim types
Response prioritization was difficult without consistent damage intelligence
Our Solution
Built pre/post-event imagery comparison pipeline for automated change detection
Applied segmentation models to classify flood, roof, and crop damage patterns
Generated fraud risk scores per claim to prioritize human review effort
Created geospatial triage dashboards for adjusters and catastrophe teams
Integrated claim workflow APIs to reduce handoff friction and cycle time
Project Timeline
Delivery span: 5 months
Data Foundation
Established geospatial data ingestion and aligned claim metadata standards.
Model Development
Trained and validated change detection and damage segmentation models.
Workflow Integration
Connected scoring engine to claims lifecycle and reviewer queues.
Operational Rollout
Activated catastrophe response playbooks with dashboard enablement.
"We can now prioritize claims with objective satellite evidence, even when field access is constrained."
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