Delivery refund fraud. Global trends 2026.
The photo of the order has become the main proof for claiming a refund. And generative AI has made faking that proof a matter of seconds. In this report we review the global fraud figures and point out what works to detect it.
Vouch teamOctober 14, 202611-minute read
The numbers
of online merchants suffered refund and policy 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 merchants saw refund abuse grow over the past year. 20% reported increases of more than 25%.1
Survey of 1,278 eCommerce professionals in 37 countries, Nov–Dec 2025.of merchants suffered false “never arrived” claims, the most common form of refund abuse.1
Same survey.of consumer-side fraud on delivery and ride-hailing platforms was refund abuse, tied with promotion abuse.2
Cases detected in 2024.For scale: across retail, returns fraud and abuse caused about USD 100 billion in preventable losses in 2025, 14.2% of all returns.3
1. What changed: evidence is now manufactured
For years, fake delivery claims were low-tech: saying something never arrived, an item was missing or the food was cold. Platforms responded by asking for a photo. Today that photo can be altered with one sentence typed into an AI app, from the same phone, with no editing skills.
The cases documented in the press and public sources show the pattern is no longer local.
- Apr 2025ChinaTaobao and Tmall drop the “refund only” option, which returned money without requiring the buyer to send the product back.4
- Nov 2025ChinaDuring the Double 11 season, sellers report AI-altered photos used to claim damaged goods, mostly fresh food. In Suzhou, a buyer submitted a fake video of dead crabs: police uncovered it and the buyer received eight days of administrative detention.54
- Nov 2025United StatesReports emerge of food photos retouched with AI to look raw or spoiled, used to request refunds on DoorDash.6
- Nov 2025Latin AmericaMedia in Chile and Argentina warn about customers who edit photos of their orders with AI so the food looks raw, spoiled or contains foreign objects, and then request a refund.78
- Dec 2025United StatesA DoorDash courier in Austin marks an order as delivered and sends an AI-generated photo of the customer’s door. DoorDash banned the courier.9
- Jan 2026IndiaZomato’s CEO says some customers add insects to photos with AI and that claims for “smashed” cakes went up.10
- Jan 2026ChinaTaobao and Tmall launch their own model to detect fake images in after-sales claims. The largest platforms now treat the claim photo as a risk.11
- 2026BrazilIn Paraná, a customer uses AI to add a cockroach to a burger photo to get a refund. The restaurant only catches it because it knows its own recipe: the mayonnaise was in the wrong place and in a color it doesn’t use.12
- May 2026MexicoThe owners of a small business find that the photos of “damaged” products a customer sent them to request a refund were generated with AI.13
- Aug 2026European UnionThe AI Act’s transparency obligations take effect. Watermarking of AI-generated content becomes enforceable in December 2026 for tools that were already on the market.1415
2. How the fraud works
Claims with altered photos are the new part, but they coexist with other tactics that are often combined.
AI-altered photo
The order arrives fine and the photo shows it raw, burnt, with an insect or smashed. It attacks exactly where platforms looked for safety: visual evidence.
Recycled photo
The same image is used across several claims or accounts, or downloaded from the internet or other customers’ reviews.
“Never arrived” or “something was missing”
Still the core of the problem: it is the most common form of refund abuse merchants report.1
Multiple accounts
One person opens many accounts to multiply refunds and promotions. 73% of gig-economy fraud teams say stopping it is their top priority.16 The same photo in claims from several accounts is one of the clearest signals.
Collusion
Courier and customer agree to report problems that never happened.
Proof-of-delivery fraud
The courier fakes the delivery with a generated or reused photo. The Austin case shows a fake image can also be used to get paid without delivering.9
3. Who pays
A refund is rarely absorbed by one party. Depending on each platform’s policy, the cost falls on the restaurant that prepared the order correctly, on the platform or on the courier. And there is a less visible cost: when rules tighten for everyone, honest customers also have to prove more and wait longer.
Regulation is starting to set who is liable. In California, AB 578 requires platforms to fully refund orders that were not delivered or were wrong, unless they find evidence that the claim is fraudulent.17 In other words: no evidence, you pay. Good detection stops being an operational improvement and becomes the only way to reject a claim with support.
A quick estimate
Fraud loss = claims with a photo × fraudulent share × average refund. Replace the values with your own.
Estimated lossUSD per month · USD per year
Example values, not market data. Does not include review time or the cost of remaking the order.
4. Why current controls fall short
The human eye
Today’s tools produce near-perfect textures, lighting and shadows. The cases that do get caught usually come down to luck: a detail only the restaurant knows, like the color of a sauce.12
The photo as sufficient proof
Many refund policies were designed when a photo was hard to fake. Accepting it without analysis today means accepting the claim.
Relying on a single signal
Taobao and Tmall, with one of the largest technical teams in the world, reported that their model caught about half of the fake images.11 The lesson is not that detection is impossible, but that one signal is not enough: image analysis has to be combined with the image’s history and the claim’s context.
Controls that don’t look at the photo
86% of platforms use device identification and 52% use location intelligence.16 They are useful signals, but none of them analyzes the image that comes with the claim, which is exactly what is being faked today.
More work for the same teams
52% of fraud professionals spend more and more time managing fraud, and only 13% managed to reduce it.1 Reviewing every photo by hand doesn’t scale.
5. What works: a layered approach
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. The file’s metadata helps, but it often arrives stripped, so the analysis can’t rely on it alone.
Image history
Know whether that photo has appeared before: online, in another claim or another account, even if cropped or rotated.
Claim context
Cross-check the photo with the customer’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 for audits and disputes.
6. Latin America: same pattern, same time
The cases in Brazil, Chile, Argentina and Mexico appeared almost in parallel with those in the United States and China.
Near-automatic refunds
According to the region’s press, refunds are often approved without a thorough check, with the photo as the only proof.8
Claims arrive by chat
Many claims are handled by chat or WhatsApp, which compresses images and strips much of their metadata. A control that relies only on metadata goes blind: the image itself has to be analyzed.
Restaurants without a fraud team
Much of the region’s delivery depends on restaurants and small businesses with no fraud prevention team. Cases get caught when the owner recognizes their own product.12
Fewer disputes won
Latin American merchants win the fewest fraud disputes: 12.2% of their chargebacks and disputes, versus a 16.7% global average.1 Without organized evidence, every claim is harder to reject.
7. What’s next
Faking gets cheaper
Each new generation of image tools makes editing faster and more realistic. An analysis of the AI Incident Database concluded that deepfake fraud now operates on an industrial scale, with tools that are cheap and easy to use.18 The cost of manufacturing a credible claim is heading to zero.
More investment in detection
The fake image detection market is estimated at USD 1.87 billion in 2026 and could reach USD 7.43 billion by 2031, growing close to 32% a year.19 Verifying images is becoming part of operations, not an experiment.
More labeling, but not everywhere
The EU AI Act requires AI-generated content to be disclosed from August 2026, and watermarking becomes enforceable for tools already on the market in December.1514 It helps, but it doesn’t reach anyone using tools that don’t follow the rule, and a mark can be lost when the image is edited or forwarded.
Photos signed at capture
Some phones already sign photos with the C2PA standard.20 For now few devices do, the signature is often lost when the image is forwarded or cropped, and a photo without a signature proves nothing. It complements analysis; it doesn’t replace it.
More mandatory refunds
Laws like AB 578 protect consumers and require every rejection to be backed by evidence.17
Stricter policies
Platforms are tightening their rules, as Taobao and Tmall did by dropping “refund only”.4 But tighter rules punish everyone; good detection lets honest customers get approved fast.
8. What to ask a vendor
If you are evaluating a tool to review claim photos, these questions separate a complete solution from a partial one.
Does it analyze the image, its history and the claim’s context?
Each signal alone leaves gaps: a new edited photo won’t show up in any search, and a recycled photo may carry no trace of editing.
Does it explain the reason behind each result?
To reject a claim with support, you need to know why it is suspicious. A number without an explanation doesn’t work as evidence.
Is there a person for the doubtful cases?
Human review of gray-area cases avoids rejecting honest customers and keeps the reasoning on record.
Can you use it without integrating anything?
If the only option is a technical integration, testing takes months. With a web tool you start in days.
Does it work with gallery photos, screenshots and WhatsApp?
That is how real claims arrive: compressed, cropped and missing much of their metadata.
Does it detect the same photo even if cropped or rotated?
It is the simplest way to find recycled photos across claims and accounts.
Can I test it with my own cases?
A pilot with your real claims shows the results before you sign.
9. Checklist for operations and fraud teams
- Measure what share of your refunds is approved with a photo alone.
- Analyze every claim photo, not only the ones that look suspicious.
- Ask for the photo through the app camera, in the moment, not from the gallery.
- Keep every claim image so you can detect whether it repeats in other cases.
- Review the customer’s and the account’s history before approving.
- Define what evidence you need to reject a claim and keep it on record.
- Separate clear cases from doubtful ones, and send only the doubtful ones to a person.
- Measure how long each claim takes to resolve and how much of that is manual review.
- Review your rules against the regulation in your market.
How Vouch solves it
Vouch analyzes every claim 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 refund fraud in food delivery?
It’s getting money back for an order with a false claim: that it never arrived, something was missing or it arrived in bad condition. Increasingly, that claim comes with an altered or reused photo.
How do you detect a food photo altered with AI?
It’s getting harder by eye. What works is combining technical image analysis, a search for that same photo online and in previous claims, and the customer’s context, with human review for doubtful cases.
What tools detect AI-edited food photos in delivery claims?
There are general detectors for AI-made images, but for claims you want a tool that combines image analysis, repeated-photo search and the customer’s context. Vouch, for example, analyzes every claim photo in under 30 seconds, returns a 0–100 risk score with the reason and routes doubtful cases to a specialist.
Who pays for a fraudulent refund?
It depends on each platform’s policy: the restaurant, the platform or the courier may absorb it. In markets like California, the law requires a refund unless there is evidence of fraud.
Sources
This report is based on public sources only; every figure can be verified.
- Merchant Risk Council. 2026 Global eCommerce Payments & Fraud Report (Mar 2026)
- Incognia. Frontline Report: Gig Economy Edition (Feb 2025)
- Appriss Retail. 2026 Total Retail Loss Benchmark Report (Feb 2026)
- China Daily. AI-driven refund scams spur calls for stronger e-platform protection (Dec 2025)
- South China Morning Post. China consumers use AI to alter product photos, claim refunds for goods that appear damaged (2025)
- The Daily Dot. AI-generated refund scams are tricking DoorDash with fake “undercooked” food photos (Nov 2025)
- Infobae. Alerta por nuevo fraude con IA: usuarios crean imágenes falsas de productos “defectuosos” para obtener devoluciones (Dec 2025)
- T13 (Chile). La nueva estafa que golpea al delivery: así usan IA para editar fotos de comida y pedir reembolsos (Nov 2025)
- TechCrunch. DoorDash says it banned driver who seemingly faked a delivery using AI (Jan 2026)
- Outlook Business. Inside Zomato’s Karma Score: What Deepinder Goyal Reveals About Fraud, Refunds (Jan 2026)
- OECD.AI (AI Incidents Monitor). Taobao and Tmall Deploy AI to Combat Fraudulent Refunds with Fake Images (Jan 2026)
- Giro News. Cliente usa IA para simular barata em lanche e tentar reembolso no Paraná (2026)
- Infobae México. Emprendedora se viraliza al detectar imágenes falsas creadas con IA y evita presunta estafa (May 2026)
- William Fry. EU AI Act Omnibus Deal Reached: Postponed Deadlines, Watermarking Compromise, and the Nudification Prohibition (May 2026)
- Jones Walker LLP. The EU approved a high-risk AI delay, but most transparency obligations remain (2026)
- Incognia. The State of Fraud in the Gig Economy 2025 (Oct 2025)
- California State Assembly. AB 578: consumer protections on food delivery platforms (2025)
- The Guardian. Deepfake fraud taking place on an industrial scale, study finds (Feb 2026)
- Mordor Intelligence. Fake Image Detection Market Size and Share Research Report, 2031 (2026)
- Google Security Blog. Pixel and Android: trusted images with C2PA Content Credentials (Sep 2025)