Synthetic "Evidence": Caught in time

Two pieces of what I can only call AI slop crossed my desk recently. Both looked credible, but both had real problems.

Is it real, or is it fake?

The first was a storm-damage photo of a silver BMW crushed under a fallen tree. Experience flagged it fast: a lifted root plate and a fractured base on the same tree, power lines running through the canopy undisturbed, garbled badge lettering. Then I cropped it.

The tells disappeared, and what remained was a tight, plausible image I doubt many would question. What exposed it was a registration lookup: the plate belongs to a convertible, and the image showed a fixed metal roof. The first image was AI generated.

The unrelated second piece of slop was an automated repair assessment from an overseas provider, apparently built largely from photographs. It identified the vehicle by silhouette, hedged nearly everything as "to be confirmed", and quoted a repair range of USD 12,000 to 30,000+. Nothing in it proved the VIN, the photo and the vehicle in front of the inspector were the same car.

This image is of an instantly modified Nissan van - the 8 page report proposed an in-depth assessment at a cost of USD2,350 to USD3,800 to reach what most of us would assume in 5 seconds or less is a total loss.

Claims now cross borders, languages and time zones, and we routinely rely on evidence from people we can't meet. Vigilance has to be structural:

→ Reconcile every image against the register: make, model, body style, colour

→ Ask for original files and treat missing metadata as a flag

→ Require consistent second and third angles

→ Tie the VIN to the vehicle with an on-vehicle photo and an independent decode

Fraud follows the cheapest path to a convincing story, and synthetic imagery just made that path much cheaper. The defence isn't suspicion of everything. It's a process: reconcile, verify, corroborate. How good are your team at verifying evidence?

#ClaimsManagement #FraudDetection #AI #Insurance #AutoIntelliDents