Can you spot the fake claim?

New images show how convincing AI-generated insurance fraud has become

With insurance fraud now pushing up the average person’s annual premium by £50, new images reveal just how indistinguishable AI-generated claims are becoming - and the subtle clues we’re all missing.

A new study by data and AI leader SAS demonstrates how generative AI can fabricate convincing crash scenes in seconds, closely mirroring the tactics fraudsters and organised crime groups are already using to deceive insurers.

According to the Insurance Fraud Register, insurance fraud has now led to an average increase of £50 on consumer annual policies - while the average cost of a fake claim has now hit £84,000, with one in seven claims proven to be fraudulent, according to Adyen.

To expose how easily the human eye can be fooled, SAS asked generative AI to create doctored insurance images. Two of the three images below are fake, but are you able to tell which ones?

At first glance, Image 1 appears to be a perfectly ordinary collision scene. In reality, the entire photo is synthetic, created using a prompt for a collision on a suburban English street.

Image 2 looks even more convincing, and the image of the yellow car is real. But bystanders have been removed, number plates have been altered, and the digitally added windscreen damage is all the work of AI. By erasing contextual clues - like people and surrounding cars - fraudsters can remove the very evidence insurers rely on.

Adam Hall, Insurance Fraud Specialist at SAS, said:

“Fraudsters are exploiting generative AI tools to make fabricated damage and doctored scenes look entirely plausible. With just a few prompts, they can create, enhance or erase visual evidence to support a false insurance claim.

“People should look for subtle inconsistencies - shadows that fall the wrong way, damage that doesn’t match the impact, blurred number plates, or backgrounds that appear too clean or empty. These tiny visual mismatches are often the first red flags of an AI-generated claim.

“But AI isn’t just empowering fraud - it’s also helping insurers fight back. AI and machine learning can detect both one-off scams and sophisticated, organised networks. By analysing huge volumes of claims data, AI can be used to reveal anomalies and patterns that humans simply can’t - reducing losses, improving accuracy, and safeguarding customers.

“As fraudsters adopt new techniques - fake identities, forged documents, digital-first scams - AI evolves too. It can review and retrain models, absorb new data sources, and deliver more accurate risk scoring to keep insurers one step ahead.”

Readers can see the full report here.

ENDS
 

Methodology

SAS leveraged generative AI to simulate and analyse multiple scenarios of common insurance fraud techniques. By issuing a series of tailored prompts, the AI generated examples and insights across different types of fraudulent activity.

This approach allowed SAS to highlight how easy it can be to manipulate images, providing a comprehensive view of the key challenges facing the insurance industry today. Where existing images were doctored, they were taken from free-to-use image sites. SAS conducted a short poll to see which images people thought were doctored and real in the images provided.

Images and prompts provided

Image 1

Create an image of two Hondas - a blue Civic and a red Accord - in a collision on a suburban English street.

Image 2

Remove all people from this image, change the registration plates of the cars behind, and add damage to the yellow car’s windscreen.