How to detect AI generated insurance claim photos and documents

Where do insurance fraud AI images show up?

AI images show up wherever a claim is settled on photos and documents the claimant supplies, without an inspection. Claims handlers, desk adjusters, special investigation units, loss assessors, brokers and fleet managers see the same kinds of files.

• Damage photos uploaded through an app: a dented car, a flooded kitchen, a cracked phone screen.
• Photos meant to prove ownership of items reported stolen or lost.
• Repair estimates, invoices and receipts submitted as PDFs or images.
• Medical notes or letters supporting injury or travel claims.
• Recorded statements or voice messages from claimants and witnesses.

How do claim photo checks differ from general fraud and KYC checks?

Claims review asks a narrower question than onboarding: does the evidence for this loss show signs of being generated? Our page for fraud and KYC teams covers identity checks, receipts and onboarding video. This page focuses on claims evidence.

Be clear about what AnyFact is and is not before you use it on claims:

• It detects signs of AI generation. It does not reliably detect manual edits, such as damage painted onto a real photo or an amount changed in a PDF editor.
• It does not spot reuse: a genuine photo from an old claim or from the internet will look human.
• It is not a claims-fraud platform. It does not score claims, link related claims or connect to your claims system, and it has no public API yet.

What are the risks of missing fake damage photos, or wrongly flagging them?

Missing a generated photo means paying for a loss that never happened, and it teaches whoever submitted it that your process can be beaten. The cost spreads to every honest policyholder through premiums.

Wrongly flagging a genuine claim hurts people at a bad moment. Real claimants send blurry phone photos taken in poor light, images brightened by their phone’s software and screenshots forwarded by a relative. A delayed or denied claim on the basis of a score can lead to complaints and regulatory attention, and it is simply unfair. Every flag should lead to more evidence, not a decision.

How to detect AI generated insurance claim photos: a review workflow

Check the claim, then the files, then escalate. This order catches most problems early and keeps detection in a supporting role.

1. 01Check the claim facts: cover on the date of loss, weather records for storm claims, the repairer or merchant’s existence, and the claimant’s previous claims.
2. 02Ask for originals: photos straight from the phone, PDFs as issued by the repairer, not screenshots or re-saved copies.
3. 03Read the metadata: capture dates, device and location against the reported date and place of loss. A content credentials checker shows C2PA and EXIF/XMP data, which may name an AI tool.
4. 04Run a reverse image search to catch photos reused from the web or from earlier claims.
5. 05Compare the photos with each other: the same damage should look the same from every angle, in the same light, on the same object.
6. 06Screen suspicious files with the AI image detector or the AI document detector, and read the signals listed.
7. 07Escalate to an investigator who can request a live video walk-around, an in-person inspection or contact with the repairer.

How should claims intake be set up to make photo checks easier?

Good intake design makes fakes harder to submit and genuine claims faster to approve. Small changes to what you ask for at first notice of loss pay off at every later step.

• Capture in the app where possible, using the camera directly rather than uploads from the gallery, so photos are taken at the time of the claim.
• Ask for a set, not a single shot: one wide photo showing the setting, then close-ups of the damage from two angles.
• Include something that ties the photo to the claim, such as the number plate in frame or the claim reference written on a note.
• Keep the files as received, with their metadata, instead of converting or compressing them on upload.
• Offer a short video walk-around for larger claims; it is harder to generate convincingly than a still image.
• Explain to claimants why you ask, so honest customers see the requests as care rather than suspicion.

Which AnyFact detectors fit claims teams, and what do they return?

Three detectors match claims evidence. Each returns a verdict (probably human, uncertain or probably AI, and mixed for documents), the signals behind it, a plain-language explanation, the limits of the method and a PDF report you can add to the claim file.

Limits for claims teams

Detection is weakest on the files claims apps often produce: compressed, resized and cropped photos. A generated image that was printed and re-photographed loses much of its digital trace. A real photo with hand-made edits, or a genuine photo of someone else’s damage, can come back probably human.

Keep detection as one input among claim facts, metadata, reverse searches and inspection. For error rates and why they matter, read how accurate AI detectors are.

Before uploading claim files, check your data protection obligations: claim photos and documents often contain personal data. AnyFact deletes uploaded files after 30 days and processes them with Modal, Supabase and Anthropic’s Claude.

Frequently asked questions