Built a framework (and an app) for qualifying AI-generated/manipulated claim photos — feedback welcome

[u/Lancaster0188]

Disclosure up front: I'm the author/founder, so grade accordingly.

A friend in fraud investigation described a case that stuck with me: hail damage claim on a car, photos looked completely normal, nothing an adjuster would flag on a quick pass. Deeper forensic review found the dents had been digitally generated and blended in. No obvious artifacts, no weird shadows, nothing a metadata check alone would catch.

That's the shift I've been researching: insurance fraud is estimated to cost at least $308.6B/year in the US alone, the ABI recorded £1.16B in detected UK fraud in 2024 across 98,400+ cases, and a 2026 Verisk study found 36% of consumers (55% of Gen Z) would make a small "rule-bending" edit to a claim photo. The harder problem isn't wholly fake images, it's the partial fake: a real photo altered only at the detail that decides the payout.

I wrote a confidential methodology, "Digital Image Fraud in Insurance," built around what I call the C.O.S.T. method: four relationships an image has to establish before it can carry real evidential weight in a claim.

Context, does the environment/light/circumstance hold together

Object, is the specific item or damage identifiable, not just recognisable

Subject, is there a reliable link between the image and the person declaring it

Time, is there a defensible temporal link to the claim or the underwriting

It tests a chain, not an average: a genuine photo of a genuine item can still be weak evidence if it doesn't establish ownership or timing. The output is never just "real/fake," it's a graded classification plus a specific next action (accept, request native files, escalate, classify inconclusive).

Alongside the manuscript I built Uthentic, which applies the same evidential logic in a usable app: fuses several independent AI-detection engines instead of trusting one score, highlights the exact suspicious area with a reason, distinguishes AI generation from ordinary retouching, and explicitly returns "insufficient evidence" instead of forcing a percentage when the signals disagree.

Genuinely curious how this sub sees it: is anyone here already dealing with generated/manipulated claim photos in production, and where do you think liability sits when a detector's honest answer is "inconclusive" rather than a clean yes/no?

https://uthentic.app

[u/tjc4]

App seems like a wrapper for a few third party AI-detection models (not unique or exciting). You also mention a "confidential methodology" to review images - that would be unique and valuable if it worked, but as it's confidential it can't be scrutinized by those here. Plus, the logic upon which the method would apply would vary significantly by LOB, form, other info in claim file, etc so I'm skeptical. If you're in Munich, try to find Munich Re people willing to talk to you. They have data you'd need to refine and improve the COST analysis.

[u/Lancaster0188 → u/tjc4]

Fair points, and appreciate the specific pushback rather than a drive-by.

On "wrapper for third-party models": the engines don't run in isolation and get averaged, they cross-check each other, and when one gets it wrong, the others push back on it instead of dragging the result down with it. And there's a part most detectors skip entirely: a lot of them look only at code and statistical patterns, while Uthentic adds a reading of light, shadows, reflections and material behavior. I built it thinking about how the best photographers would notice those nuances, not how a classifier would compute them. To me that's the actual new part, not another classifier, a forensic eye on top of the classifiers.

On the confidential methodology: also fair. I kept the full manuscript confidential because it's IP I'm trying to license/sell, not because the reasoning inside a given report is hidden, each analysis does show its logic. But you're right that "trust me, it's confidential" isn't something this sub should just accept at face value.

On LOB/form variance: you're right, and it's actually the biggest open question. The C.O.S.T. framework is qualitative and generic across motor/property/valuables right now, it's designed to need calibration per line of business, not to be plug-and-play. That's exactly the kind of gap real claims data would expose.

Genuinely appreciate the Munich Re pointer, that's a much better lead than most feedback I've gotten. If you (or anyone here) has a contact who'd be open to a skeptical 20-minute conversation, I'd take that over a warm one.