Solutions
Insurance claims fraud detection
Verify claim photos, videos, and documents are genuine — not staged, edited, or AI-fabricated.
$308B
the annual cost of insurance fraud in the U.S.Source: Coalition Against Insurance Fraud
- Detects AI-fabricated damage photos
- Flags edited and composited claim images
- Evidence attached for your adjuster
The threat
Claimants generate damage photos that never happened, edit real images to exaggerate a loss, and fabricate supporting paperwork with consumer AI tools.
From FNOL to result without slowing the claim
Detection runs at first notice of loss: each uploaded photo, video, or document is scored by the REST API with evidence attached. Clean media stays straight-through, and flagged media routes to an adjuster or SIU without changing the claimant experience.
The new fraud is editing reality, not inventing it
The hard problem is exaggeration, not fabrication: AI-extended damage or one changed digit passes every metadata check, because only the pixels lie. Eva V1.6 detects localized manipulation inside otherwise genuine images and localizes the edited region (insurance fraud already costs the U.S. $308B a year).
/ROI
What this is worth
- Loss avoidance: as an illustration, at 50,000 checks a month, catching just 0.2% more synthetic media is ~100 frauds stopped — at a $10k average loss, that's ~$1M a month that never walks out the door.
- Review time: evidence-backed results cut manual review from minutes to seconds, so analysts handle the small flagged fraction instead of screening everything.
- One integration: a single REST API covers faces, documents, and devices — no second vendor, no second review queue.
Common questions
How do insurers detect AI-generated or edited claim photos?
AI-generated and edited claim photos are caught by forensic analysis of the image itself: generation artifacts, inconsistencies in lighting and texture, signs of localized editing, and traces left by manipulation tools. ScamAI applies these checks to every photo, video, and document at upload through the REST API, returning a scored result with evidence attached, so adjusters know which files to trust before adjudication begins.
What is a shallowfake in insurance fraud?
A shallowfake is a genuine photo or document altered with basic editing tools rather than generated from scratch — real damage extended, a date changed, an amount inflated. Shallowfakes are harder to catch with context checks because the underlying media is authentic; the manipulation is localized. Detecting them requires pixel-level forensics that identify edited regions inside otherwise real images. ScamAI flags both shallowfakes and fully AI-generated media, and tells the adjuster which kind it found.
Does fraud detection slow down legitimate claims?
Media screening runs automatically between upload and adjudication, so genuine claims are not delayed — a clean result keeps the claim on the straight-through path with no extra step for the claimant. The effect is usually the opposite of friction: because fabricated and manipulated media is filtered out with evidence attached, adjusters spend less time on manual suspicion and more legitimate claims qualify for fast-track handling. Fraud detection at intake is what makes higher automation rates safe.
Ready to run it on your own claims media?
15 minutes, on your own media. Pick a slot and leave with a result.