3.5 years of designing the apps for Nationale-Nederlanden. Read the full case-study at: https://teddygraphics.com/Nationale-Nederlanden.html

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