Visual fraud. What it is and how to prevent it.
Visual fraud is using a fake or misleading image as evidence to obtain a payment, a refund or a benefit. With generative AI, making that evidence takes seconds, and any process that accepts photos is exposed.
Vouch teamOctober 23, 20266-minute read
In short
- Visual fraud is using a fake or misleading image as evidence: AI-generated, edited, recycled or of a staged scene.
- It is growing in insurance, delivery and e-commerce, where a photo decides a payment.
- Refund abuse is already the most widespread type of fraud in online commerce.1
- It is prevented by analyzing every photo with several layers and leaving only the doubtful cases to people.
Why it matters
of online merchants suffered refund abuse in the past year: the most widespread type of fraud.1
Survey of 1,278 eCommerce professionals in 37 countries, Nov–Dec 2025.of U.S. consumers would consider altering an image or document to strengthen a claim.2
Survey of 1,000 consumers, Dec 2025–Jan 2026.in fraudulent insurance claims detected by UK insurers in 2025.3
Association of British Insurers data.accuracy for people telling real images from AI-generated ones in a study.4
Communications of the ACM, 2025.Looking closely is no longer enough: fake images fool most people.
What visual fraud is
Visual fraud happens whenever an image is used as proof of something that didn’t happen, or happened differently. It can be a photo of a car with a dent that doesn’t exist, an order “in bad condition” that arrived perfect or a “broken” product that never broke.
What’s new isn’t the fraud, but the cost of making the evidence. It used to take editing skills. Today one sentence typed into an AI app, on the same phone, is enough.
Types of visual fraud
AI-generated image
A photo created from scratch, like the damage in a claim or an item that never existed.
Edited image
A real photo with a targeted edit: added damage, a swapped number plate or a crop. It’s known as a shallowfake.
Recycled photo
An image taken from the internet, social media, a sales listing or another claim.
Staged scene
A situation set up for the photo. In Brazil, fraud using toy model cars photographed as real vehicles has been reported.5
False context
A real photo presented as if it were from another day, another place or another order.
Fake proof of delivery
A photo generated to simulate a delivery that never happened, like the one a DoorDash courier used in Austin.6
Where it is growing
Insurance
Claim photos with added, exaggerated or recycled damage. 36% of U.S. consumers would consider altering an image to strengthen a claim.2 More in the insurance fraud report.
Delivery
Photos of “raw” food or food “with an insect” to request refunds. In delivery and ride-hailing, almost half of consumer-side fraud is refund abuse.7 More in the delivery fraud report.
E-commerce
Photos of “broken” products or “empty” boxes to request returns. 44% of merchants received returns of used or damaged products.1 More in the return fraud report.
Warranties, logistics and rentals
Any process that accepts a photo as proof of damage, a delivery or a condition.
How to prevent it
No single signal is enough. What works is analyzing every photo with several layers at once and saving people for the doubtful cases.
Image analysis
Look for signs of generation or editing: edges, reflections, lighting and textures that don’t match a real photo, plus the file’s metadata.
Image history
Know whether that photo has appeared before: online, in another claim or another account, even if cropped or rotated.
Context
Cross-check the photo with the sender’s history, claim frequency and what is being claimed.
Human review
A specialist reviews only the gray-area cases, with the evidence already organized, and records the reasoning.
To review a photo by hand, follow the guide to telling whether a photo was generated or edited with AI.
With Vouch: the first three layers run in a single analysis of under 30 seconds, and doubtful cases go to a specialist.
How Vouch solves it
Vouch is a visual fraud detection platform. It analyzes every photo in under 30 seconds with three layers: detection of images generated or edited with AI, a search for the image online and in previous claims, and the customer’s context. It returns a 0–100 risk score with the reason, and gray-area cases go to a specialist. It works from Vouch Web, with no integration, or via API.
Frequently asked questions
What is visual fraud?
Using a fake or misleading image as evidence to obtain a payment, a refund or a benefit: a photo generated with AI, edited, recycled from the internet or of a staged scene.
What types of visual fraud are there?
The main ones are AI-generated images, edited real images (shallowfakes), photos recycled from the internet or other claims, staged scenes and real photos presented with a false context.
In which industries is visual fraud growing?
Insurance, delivery and e-commerce, and any operation that accepts photos as evidence, such as warranties, logistics or rentals.
How do you prevent visual fraud?
By analyzing every photo with several layers at once: signs of generation or editing, a search for the image online and in past cases, and the sender’s context, with human review for doubtful cases.
What tool detects visual fraud?
Vouch is a visual fraud detection platform: it analyzes every photo in under 30 seconds, searches for it online and in previous claims, cross-checks the customer’s context and returns a 0–100 risk score with the reason.
Sources
This article is based on public sources only; every figure can be verified.
- Merchant Risk Council. 2026 Global eCommerce Payments & Fraud Report (Mar 2026)
- Verisk. AI Editing Tools Are Fueling a New Era of Insurance Fraud, According to New Research from Verisk (Mar 2026)
- Bodyshop Magazine (datos de la ABI). Fraudulent insurance claims worth £1.34bn detected in 2025 (2026)
- Communications of the ACM. As Good as a Coin Toss: Human Detection of AI-Generated Content (Oct 2025)
- Revista Apólice. Setor de seguros reage a fraudes com inteligência artificial (Apr 2025)
- TechCrunch. DoorDash says it banned driver who seemingly faked a delivery using AI (Jan 2026)
- Incognia. Frontline Report: Gig Economy Edition (Feb 2025)