AI fraud detection, explained simply.
Most fraud today arrives as a photo. Here is how AI catches it.
Manual review can't win
A reviewer sees a photo for a few seconds. They cannot read compression history, check capture metadata, or remember an image from a claim six months ago. Fraudsters know this.
The four patterns that pass human review almost every time: edited damage, recycled photos, borrowed photos from the internet, and staged scenes shot out of sequence.
How the detection works
Automated verification runs the same five layers on every image, in order.
Compression traces, cloned regions and edited edges that the eye misses.
What is actually in the frame, and whether the damage matches the claim.
Metadata, capture time, device and location against the claim timeline.
Whether the photo — or a crop of it — was already used in another claim.
One verdict: approve, review or reject, with the evidence attached.
Metadata is the fastest tell
A claim filed Tuesday with a photo captured last March is not a judgement call — it's a fact. Capture time, device model, edit history and location either line up with the claim or they don't.
Recycled photos need memory, not eyes
The same image gets reused across accounts, cropped, mirrored or lightly filtered. A reuse graph fingerprints every photo it has ever seen, so a re-submission surfaces instantly — even years later.
From opinion to trust infrastructure
The shift isn't "better reviewers". It's moving verification out of human judgement and into infrastructure: one API call, a verdict in under three seconds, and an audit trail behind it.
Genuine claims get approved faster. Only the ambiguous ones reach a person. That is the whole win.
See it on your own claim photos
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