Short answer
ai image fraud insurance claims is a different problem from newsroom verification. A claimant controls the subject, the timing, and the camera, so absence of provenance is expected rather than suspicious. The useful question is not whether a photo was generated but whether the damage narrative is internally consistent across everything the claimant submitted.
Why the usual signals fail here
Most detection guidance assumes an adversarial third party passing an image along. Insurance claims invert that: the person submitting the photo also owns the object being photographed and usually has the original file. A missing C2PA record proves nothing, because consumer cameras rarely produce one. A stripped EXIF block is equally ambiguous, since the claim portal may normalise uploads on receipt.
What actually carries weight
- Sequence consistency: several photos taken minutes apart should share lighting direction, weather, and background detail. Inconsistency across a set is more informative than any single score.
- Metadata continuity: capture timestamps, device model, and GPS should stay coherent across the batch. A single photo with a different device model is worth a question, not an accusation.
- Damage plausibility: whether the visible damage matches the described incident. This is a domain judgement, and no detector substitutes for it.
- Submission history: repeat claims, reused images, or photos that reverse-search to an earlier listing.
Where detection helps
A detector is useful for triage at intake. It flags files that do not resemble a camera original, which can indicate screenshots of screenshots, stock imagery, or heavily recompressed material. That is a routing decision, not a fraud finding. A high score is not proof of fabrication, and presenting it as such will not survive a dispute.
A workflow that holds up
- Preserve the file as received, including the original upload rather than a portal thumbnail.
- Run provenance, metadata, and detector checks and record each result separately instead of collapsing them into one number.
- Compare the batch for internal consistency before judging any single image.
- Where signals conflict, escalate to human review and state which specific check raised the doubt.
What this cannot do
It cannot establish that a photo is genuine. It cannot recover metadata stripped before submission. And it cannot distinguish a fabricated claim from a genuine claim supported by poor photographs, which is the more common case. Read the methodology for how the layers combine, or look at sample reports to see how conflicting signals are documented.
If you need provenance, metadata, and detector output on one file in a single view, start a free review and read all three before writing a decision.