AI-powered refund abuse and dispute fraud: The democratization of deception

There’s no question about it: There’s an emerging threat of refund abuse powered by sophisticated AI image and video editors.

Let’s dive into how AI is used for harm by refund abusers and fraudsters, the primary attack methods, and frameworks for detection and prevention.

Digital “proof” created by AI, used for refund abuse

A new wave of refund abuse is upon us. Both professional fraudsters and first parties are dipping their toes into the opportunistic abuse made possible by widely available GenAI models – such as ChatGPT, Gemini, Claude and others – to realistically edit pictures of products, making them look dirty, torn, broken, damaged or otherwise faulty.

Ravelin recently discovered that 65% of consumers say that AI has made it easier to falsely claim refunds for items bought online. The results of this survey of 6200 shoppers, as well as the methodology, were published in the State of Refunds 2026 survey report.

For years, ecommerce businesses have built their customer support and refund processes on a foundation of trust, where a photograph serves as reliable proof of a claim. A customer states an item arrived broken and sends a picture; the case is closed and a refund is issued.

AI image and video editors fundamentally break this model by attacking its core assumption: that photographic evidence is authentic. Your eyes can no longer always be believed.

How AI-powered refund abuse works

At its heart, this new wave of refund abuse is driven by a single, powerful principle: the manipulation of digital evidence submitted to merchants. The steps can be simple:

1. A customer places an order online.

2. Upon receipt of a satisfactory product, the customer takes a picture of it inside its packaging.

3. The customer uploads the picture to one of several GenAI (generative AI) tools – these can be fraud-specific or generally available, legitimate tools. The prompt requests the addition of tears, open seams, or other issues to the product. This varies depending on the type of product but can include chipping, breaking, tearing, discoloration, etc.

4. The customer files a refund request, providing the generated image as “proof” that their product is problematic.

From here, it’s possible that the merchant does not request that the product is returned to them.

Fraudsters know that merchants often have no incentive to have the damaged item returned to them. It’s costly to have shipped back and creates the problem of handling and disposing of an item that can rarely be resold.

For some items, there is no legitimate way to return – e.g. broken glass and ceramics, food, perishables, electronics with faulty batteries.

If they are, however, asked to return the product to receive the refund, the fraudster may replace the product with a dummy or bricked product, return an empty box, or otherwise hope their fake return is not identified as such before they receive the money.

In some cases, it doesn't end with the merchant: The abusers can file a dispute with their card issuer bank using the same fake "evidence" even if the merchant rejects their claim.

This secondary attempt at old-school "friendly fraud" will mean a chargeback for the merchant, if the claim gets accepted by the bank.

How GenAI changed the game for refund abuse: The democratization of deception

At the moment, 53% of consumers are experimenting with generative AI, according to Deloitte – and 59% agree that AI supercharges refund abuse, according to our recent survey.

For those who like to bend the rules and take advantage of companies, the popularity of these tools has led to a democratization of deception. Previously, creating convincing fake images required significant skill and the ability to use complex software.

But AI tools make this trivially easy. It’s already more than realistic enough – and it’s only getting better – rapidly so. The results can be achieved in seconds by simply uploading a photo and typing a request. The tech barrier to committing abuse and fraud has effectively been eliminated. And 46% of merchants already voice concerns about AI being weaponized to enable fraud.

It should also be noted that professional fraudsters are increasingly colluding with consumers to both help them get refunds (for a cut of the profit) and to sell them tech and tools to enable refund abuse.

As part of this, refund-fraud-as-a-service tools seen around the dark web and on social media now include custom AI models which are trained by criminals to achieve more convincing results when generating fake refund evidence.

The photo-as-truth bias

Meanwhile, customer service agents are trained to resolve issues quickly, prioritize customer retention and happiness, and are used to accepting photos at face value. They’re looking for visual confirmation of a claim; they’re not conducting forensic analysis of an image file.

Abusers are exploiting this human element and will continue to do so, knowing “good enough” fake images are likely to pass a quick visual inspection.

Unfortunately, the processes and setups that exist were implemented back when generative AI images were a dream, not a reality.

Asymmetric risk

For the fraudster, the calculation is simple: The cost and effort to generate a fake image are near zero, while the potential reward is a 100% refund on an item they already possess in perfect condition.

It's a low-risk, high-reward scenario that incentivizes fraudulent behavior.

Ultimately, these AI tools give the masses the power to create a convincing reality that aligns with their claim, turning a merchant's own evidence-based refund policy into a weapon against them.

Types of AI-powered refund abuse

To better be able to detect and block them, it is useful to categorize these types of threats, based on the abuser’s target, methods and motive.

Category 1: Low-value "inconvenience" fraud

This targets low-cost items where the merchant is unlikely to set up return shipping. The goal is to get a free product by making a return financially illogical for the business.

If successful, it means the consumer has their cake and eats it too, both receiving their money back and keeping a perfectly good product.

Example scenario: A customer buys a $15 t-shirt. They receive the correct item but use an AI tool to generate a photo of it in the wrong size or wrong color. They expect the merchant won't pay £5 for a return label on a £15 item and will likely just issue a refund.

Category 2: Prohibitive return fraud

This type of scheme targets expensive, bulky or impossible to return items where return logistics are a nightmare. The fraudster creates "evidence" of such catastrophic damage that the merchant writes the item off.

The hope here is that the merchant will take the picture at face value. In some cases, the type of issue claimed might make it especially difficult to return the item – for example, broken glass or especially bulky items.

Example scenario: A customer orders a large mirror costing $400. The mirror arrives in perfect condition. They use AI to generate an image that makes the mirror look smashed. The logistics of finding a specialist carrier to collect broken glass is uneconomical for the courier. The fraudster also cites safety concerns about handling the broken mirror and therefore makes it clear they will be disposing of it and not handling it.

Refusing this refund would inevitably lead to an unwinnable chargeback. And it’s more common than one would expect. For instance, battery-containing electronics require specialist couriers, whose services are more expensive.

Category 3: Perishables no-return abuse

This very common scheme targets food and grocery delivery, where returns are impossible both because of the type of goods delivered, which cannot be resold, and because of a lack of returns infrastructure.

These merchants already face rife missing item fraud and quality issues; they now have to also manage AI generated refund claims.

Evidence in this category is easy to fake and impossible to verify, creating a headache for food delivery marketplaces.

Example scenario: A customer orders a pizza. The customer takes a photo of the pizza immediately and uses an AI model to make the pizza look burned. Since the restaurant can't inspect the item, they're often forced to issue a refund to avoid a bad review or a dispute.

Category 4: Used-then-damaged wardrobing

This category of AI-assisted refund abuse is an evolution of wardrobing – the practice of ordering something intending to wear it once and then return it – and targets fashion retailers, mainly.

GenAI can flip wardrobing on its head:

1. The refund abuser takes a photo of the item as soon as they receive it.

2. They use AI to add fake evidence of damage to the product, which still has all its tags intact and is fresh out of the box.

3. They file a request for a refund due to damaged goods.

Once the retailer accepts and requests for the item to be returned, the customer has carte blanche to wear it until the end of their return window.

They will then damage the product before returning it – roughly in the way depicted by the AI.

This doesn’t need to be perfect: They can send the item back knowing they submitted photographic “proof” of damage from the day of receipt, with all the tags still attached.

Example scenario: A consumer buys a $840 dress for a wedding. They take a photo and use AI to add a large tear on the back of the dress. They claim it arrived this way, knowing a severely damaged item can't be resold. If the merchant wants the item back, the abuser can put the hole in it – otherwise, they keep the dress and get their money back.

Category 5: Service evidence manipulation

This type of fraud applies to rentals and services where the state of an item or space at different times is key. For example, this could be car rentals or accommodation bookings.

On a simple level, a consumer may use AI to hide damage they have caused, thus avoiding paying penalties and remediation fees.

Because GenAI models can generate a series of pictures with consistency, those who wish to defraud companies can even set up consecutive pictures with different amounts of damage, being able to create a complete, fraudulent timeline.

In the accommodation/travel sector, GenAI makes it easy to write inaccurate complaints with generated evidence showing bug infestations, stains on bed sheets and other issues to receive refunds on hotel or accommodation stays once a customer has checked out.

Example scenario: A customer rents a car and causes a scratch or damage during their rental period. At the end of the rental, they choose to do a contact-free drop off. This requires photos taken of the car at a designated location. The customer uses an AI tool to edit their "after" photos and remove the damage. If the damage is later identified, the consumer argues that there was no damage when they returned the car, and they present the generated image(s) as evidence.

A robust framework to block GenAI refund claims

Unfortunately, the sky is the limit when it comes to AI-enabled refund abuse and dispute fraud.

Both opportunist consumers and professional criminals are continuing to devise new ways to defraud companies, including with GenAI for refund claims and chargeback requests.

Combating this urgent threat requires a dynamic, technology-driven framework designed to introduce hurdles that are trivial for honest customers but significantly difficult for fraudsters.

The framework combines different types of defences, all working together to solve the issue:

Level 1: Passive and automated defenses

Working in the background, tech solutions analyze the customer as well as the submitted evidence, without adding friction.

• EXIF and metadata analysis: This is a simple first check. An image file lacking any metadata, or with metadata showing it was passed through known editing software, can be automatically flagged for manual review or the claim flagged for deeper checks.

• Refund abuse fraud solutions: AI-powered refund abuse detection makes use of all the information about a customer and transaction, including their historic data, to estimate the level of risk involved with a refund claim.

• Device and image fingerprinting: This can flag mismatched data. Every camera produces images with a distinct digital fingerprint – a specific resolution, aspect ratio, compression type, and color profile. AI models also have their own fingerprints, often outputting images at a standard resolution (e.g., 1024x1024 pixels). If a customer submits a photo supposedly from a high-end smartphone but its technical properties match a known AI model's output, it can be flagged as suspicious.

• Emerging flagging technologies: Tools such as Google's SynthID are designed to watermark AI-generated content in a way that is invisible to the human eye but detectable by an algorithm. As these technologies emerge, there will be opportunities to integrate these tools into your stack to provide a powerful automated filter.

• Link analysis using graph networks: A large portion of the refund abuse affecting merchants comes from a very small group of repeat fraudsters. Graph network link analysis is a great way to easily identify hidden connections between customers. For example, abusive customers who create multiple accounts to hide the true number of refunds they’ve claimed.

Level 2: Low-friction verification requests

When focusing on the image of the claim, or when your fraud stack has flagged a request as otherwise suspicious, there are a number of steps you can take as part of your refund claim verification:

• Request multiple photos/angles: It's exponentially more difficult to create three or four images from different angles that are all consistent in their fake damage or scenario. You might, for example, want to try asking for a close-up of the damage, one from a few feet away, and one of the entire item.

• Request video evidence: A short, 10-second video panning around the item is much harder to fake than a static image – although not impossible.

However, this can’t be the cornerstone of your approach – not for long. The generative AI tools are getting better and better; chances are the above will no longer work fairly soon.

Level 3: High-friction and definitive verification

When investigating the claim of a high-risk customer (whom you still don’t want to block) or if the item is of high value, consider the following:

• In-app camera submission: For merchants with a mobile app, this is a game-changer. This policy requires that all evidence for a return must be captured live, using the camera function within the merchant's app. This bypasses the user's camera roll entirely, preventing them from uploading an edited image. The consumer arguably experiences even less friction this way, as they don’t have to leave the app.

• Live video assistant calls: For a major claim, a customer support agent can initiate a brief video call. A consumer who has faked damage to their item will be unable to comply and will often abandon the claim.

• Strategic forced returns: Even when it's not financially worth it, insisting on a return can be a powerful deterrent. A fraudster often just wants a free item, not the hassle of packaging and shipping it back, and may again choose to drop their claim.

• Leverage in-store returns: Mandating a "Buy Online, Return In Store" (BORIS) policy for flagged refund claims (or simply for all refunds) moves the fraud attempt from the anonymous digital world into the real world, thus limiting the likelihood the fraudster continues to push for a refund.

The cornerstone of your approach: Assess the customer, not just the claim

Perhaps the most critical strategic shift you can make to protect against the onslaught of GenAI-generated fake evidence is to move from a reactive, claim-by-claim validation process to a proactive, customer-centric risk assessment model.

The advanced verification techniques listed above can be powerful, but they can also introduce friction for your honest, loyal customers. Subjecting a VIP customer to a high-friction video call over a $30 damaged item is a good way to lose their business forever.

The solution is to apply friction dynamically, based on risk. Your refund abuse detection and prevention system should be built to trust your best customers implicitly while rigorously verifying the suspicious ones.

This is where Ravelin excels. Because we look at customers holistically, taking into account their lifetime history, we provide a true estimate of the amount of trust a customer should inspire.

No matter whether they request a lot of refunds or just a few, the entirety of their activity, device, location and other data will inform the recommendation – not just a single transaction.

Following this logic, instead of asking "Is this photo of a broken mirror real?", the primary question should be "What is the likelihood this specific customer is attempting abuse?"

This is answered by building a holistic risk score based on data you already have:

• Transaction history: What is the customer's return and refund request rate compared to the average? What is their purchase frequency and average order value?

• Account history and behavior: Is this a brand-new account with a large first order? How quickly after delivery did they file a claim? What is the customer’s lifetime value (LTV)?

• Reputational data: Does the email address use a reputable domain or a disposable email service?

• Demographics and item data: What is the level of risk posed by this type of device? Location? Is the item one that has historically invited a lot of fraud for the merchant? What is its value?

• Consortium data: Is the data associated with this customer high-risk across your fraud vendor’s network of merchants? And is this information objective (e.g. identified fraud) or subjective (manual reviews that were not verified)?

Segmenting customers to inspire loyalty

By combining factors like these, you can segment customers into tiers. For example, you can have trusted, standard, high-risk and/or VIP customers.

How you approach each is up to your fraud tolerance and customer retention goals – but here is one example:

• Trusted and VIP customers might get instant, no-questions-asked refunds because the lifetime value of keeping them happy far outweighs the minimal risk (after ensuring there is no likelihood of account takeover, of course).

• Standard customers might go through the low-friction level 2 verification processes as outlined above – or have other types of checks and balances introduced.

• High-risk customers would automatically be funnelled into the high-friction level 3 pathways, where they have to spend time proving the legitimacy of their claim, if it is indeed legitimate.

Such a dynamic, intelligent and risk-based approach will ensure that your fraud and abuse detection measures are a targeted shield that protects your business, not a blunt instrument that alienates your best customers.

Related resources

• Webinar on-demand: Refund abuse – quantifying, preventing, and getting buy-in
• 5 types of refund abusers and how to deal with each
• Refund vs chargeback: Why it pays to know the difference
• Experts share 5 return & refund abuse prevention strategies (from the stage of RavCon)
• The State of Refund Abuse – 2026 consumer survey report