Ecommerce return fraud trends: where refund abuse is headed and how AI keeps merchants safe

The first quarter of 2025 delivered a sobering headline for every online retailer. On 12 March the Merchant Risk Council released its Global eCommerce Payments and Fraud Report and confirmed that refund or policy abuse is now the leading loss driver for digital merchants for the second consecutive year. Already in the year before, Appriss Retail published refund abuse statistics that put a dollar sign on the problem: United States merchants issued an estimated 685 billion USD in product returns in 2024 and roughly 103 billion USD of that mountain was fraudulent. The figures make one message clear: return fraud has grown from operational headache to board-level risk.

This article takes a deep look at today’s ecommerce return fraud trends, explores the future of refund fraud, dissects the latest e-commerce fraud risk reports and offers a practical view on how AI in e-commerce fraud detection can turn the tide without punishing honest shoppers.

The refund reckoning: why return fraud keeps climbing

Free returns became a competitive advantage during the pandemic and quickly hardened into customer expectation. Today the average apparel shopper thinks of the bedroom mirror as the fitting room and treats the return label as part of the purchase price. When economic pressure escalated throughout 2023 and 2024, a growing share of consumers started to misuse that freedom. What began as wardrobing has evolved into a sophisticated ecosystem that now includes professional refund brokering services on Telegram, social videos that teach online shopping fake returns step by step and cross-border reshipping networks that exploit policy gaps between regions.

Four structural factors accelerate the trend:

• The convenience loop. Fast refunds issued the moment a parcel is scanned in the carrier network reinforce the behaviour and have conditioned customers to expect instant gratification.
• A widening gap between fulfilment scale and inspection capacity. Warehouses racing to meet next-day delivery targets rarely have the time or talent to validate every damage claim.
• Marketplace competition. Marketplaces reward low-friction policies in their seller metrics which discourages individual merchants from tightening verification in fear of ranking penalties.
• Disruptive tech availability. Generative image tools that used to require GPU clusters now run in any browser and empower even low-skill users to create shallowfake evidence of product faults.

While each factor alone is manageable, together they create a perfect storm that pushes refund losses into territory usually associated with payment fraud.

New tactics, new threats: the rise of shallowfake damage claims

Return abuse once followed familiar scripts such as the rock-in-a-box or label switching. The latest wave introduces synthetic or lightly manipulated imagery to trick automated approval workflows. Investigators call this class of forgery a shallowfake because it lacks the cinematic ambition of deepfakes yet still fools the human eye at first glance.

Popular playbooks observed in 2024 and early 2025 include GAN-generated scratches or dents, Photoshop layering that blends stock breakage onto genuine product images, metadata laundering that strips Exif history and duplicate blasts where the same image is submitted to multiple merchants carrying the identical SKU. Because most return portals rely on lightweight visual checks these shallowfakes glide through the net. The emotional weight of a damaged product combined with the fear of social media backlash often pushes customer service agents to approve without escalation.

Building a future-proof defence with AI

Layered fraud prevention is no longer limited to checkout flows; it must continue deep into the post-purchase journey. Modern image and document forensics provide three levers that align with that trajectory:

Authenticity analysis. The Fraud Scanner from Vaarhaft applies multiple analysis modules to each uploaded image or PDF. One of these modules checks whether the file was generated by AI or edited with software such as Photoshop and a manipulation heatmap then highlights suspicious areas so an agent can focus on real anomalies. That process can routinely uncover shallowfakes like dents on electronics or cloned scratch patterns on luxury handbags.

Metadata and C2PA inspection. When a customer claims a fresh smartphone arrived shattered but Exif data shows the photo was produced months earlier or the C2PA chain is broken, the case escalates automatically. Readers interested in the standard and its limitations will find a balanced view in the Vaarhaft article C2PA under the microscope.

Duplicate image fingerprinting. Return abusers often share template photos across multiple identities. The Fraud Scanner duplicate detection module hashes every frame into fingerprints and compares them against previous submissions. As the actual image data is not stored, this module is completely GDPR compliant. The same technique can protect against fake product images in e-commerce stores too, as discussed here.

Live recapture for high-risk cases. When static analysis flags uncertainty the workflow can pivot to SafeCam. The customer receives a link that opens a secure web-camera session in the mobile browser. They must record the unpacking and show the product from multiple angles. The images are then verified instantly. Abusers who depend on staged studio lighting or stock footage can rarely comply and usually withdraw the claim.

Once these measures feed insights back into policy engines merchants can move toward dynamic trust scoring. Low-risk shoppers glide through instant refunds while high-risk profiles face additional verification steps. The hybrid approach removes manual labour yet still preserves customer satisfaction scores.

Five predictions that will shape returns by 2026

1. Verify-first flows will become standard for goods above 200 USD. Automated inspection tools will approve most claims within minutes which offsets the slower flow for genuine customers.
2. Public companies will start flagging return fraud in annual reports alongside shrinkage and chargeback cost. Investors will demand clarity on mitigation roadmaps.
3. Multi-merchant fingerprint consortiums will appear. By sharing hashed imagery across non-competitive sectors retailers will spot serial abusers without breaching privacy rules.
4. Consumer image editors will ship one-click damage templates. Expect a spike in shallowfake volume when this rolls out.
5. Privacy regulations will tighten. European regulators already discuss proportionality principles that balance consumer rights with merchant protection which puts vendors that process only hashed data, such as Vaarhaft, ahead of the curve.

Ecommerce return fraud trends signal a permanent change. Fraudsters no longer need stolen cards or insider data; a convincing but fake dent in a photo can refund hundreds of dollars in seconds. The future of refund fraud will ride on the accessibility of generative tools and on how quickly merchants adopt equally intelligent countermeasures. AI in e-commerce fraud detection has reached maturity and is ready for mainstream deployment. Merchants that embed image authenticity, metadata validation and secure live recapture into their return flow can convert the current risk into a competitive advantage.

If you would like to see how image fingerprinting or SafeCam recapture fit into your existing RMA workflow, request a short demo with a Vaarhaft solutions specialist or explore additional insights in our post Detect fake product images in e-commerce.