Imagine this scenario: e-commerce platforms are achieving record-breaking sales, but the celebration is dampened by mountains of returned packages. For retailers, returns represent both a necessary component of customer satisfaction and a potential black hole for profits. More troubling is the silent erosion of profit margins caused by fraudulent activities hidden within legitimate returns.
According to estimates from Happy Returns, a reverse logistics company owned by UPS, the U.S. retail industry loses approximately $76.5 billion annually to return fraud—nearly 1 in every 10 returned items involves fraudulent activity. Faced with this alarming situation, retailers urgently need more effective methods to identify and prevent return fraud.
AI Steps In: Precision Detection to Combat Fraud
To address this challenge, Happy Returns is actively exploring the application of artificial intelligence (AI) technology in return fraud detection. The company has partnered with select clients including apparel retailers Everlane, Revolve, and Under Armour to test its AI-powered fraud detection tool, Return Vision.
This tool aims to help retailers identify fraudulent returns by flagging suspicious packages, analyzing their contents, and submitting them to human reviewers for final verification. A common fraudulent practice involves customers requesting refunds but returning lower-value substitutes—such as cheap knockoffs that cannot be resold.
Return Vision operates by first analyzing data from consumers initiating online returns to flag anomalies. These include returns initiated before or shortly after product delivery, return requests submitted by consumers linked to multiple email addresses, and returns initiated by individuals with histories of suspicious activity.
When employees at return collection points scan unwanted items into the Happy Returns system, they can view photos of what should be returned and reject obviously mismatched packages. At sorting centers, human reviewers open flagged packages. Photos taken by reviewers are fed into the AI tool, which compares them against images and other information about the expected returns. A human team then reviews the AI's assessment to make final determinations.
David Sobie, CEO of Happy Returns, stated that Return Vision testing began in early November, with more retailers scheduled to trial the tool during the holiday return peak season. He emphasized that the tool can detect subtle discrepancies that humans might miss, making fraud detection more effective.
Retailers' Pain Points: Profit Erosion and Operational Costs
For retailers, returns inherently impact profits due to accumulating costs from shipping, product refurbishment, and shelf restocking. Jim Green, Director of Logistics and Fulfillment at Everlane, noted that being unable to recover physical goods compounds the problem, costing his company hundreds of thousands annually. He revealed that Happy Returns' drop-off and consolidation network handles 85% of Everlane's domestic online returns in the U.S.
Return fraud not only causes direct financial losses but also increases retailers' operational costs. Investigating and processing fraudulent returns requires additional resources, creating further burdens. Moreover, return fraud can damage retailers' reputations and erode consumer trust in their brands.
Box-Free Returns: Convenience With Risk
Happy Returns specializes in box-free, label-free return services. Consumers can bring unwanted items to nearly 8,000 "return bars" located in Ulta Beauty, Staples, or UPS stores, where staff scan, bag, and label items before consolidating them into larger boxes for daily shipment to processing centers—saving retailers time and money. This convenient model is popular among consumers (and fraudsters alike) because it often enables instant refunds.
However, while streamlining the return process, the box-free model also increases fraud risks. The simplified process makes it easier for fraudsters to exploit vulnerabilities—for instance, by substituting cheap knockoffs or taking advantage of instant refund mechanisms.
AI's Limitations: Not a Universal Solution
Despite its advantages in detecting return fraud, AI technology isn't infallible. Happy Returns executives note that their AI program can only identify incorrect returns—not other issues like "wardrobing fraud," where customers return worn or damaged items.
Furthermore, AI's effectiveness depends on data quality and algorithm accuracy. Biased data or imperfect algorithms may lead to false positives, where legitimate returns are mistakenly flagged as fraudulent, potentially harming consumer rights.
Industry Competition: Major Players Enter the Arena
As the returns market grows, UPS competitors Amazon and FedEx also offer box-free return services, with the U.S. Postal Service preparing to implement similar offerings. Amazon stated that its return service similarly uses automated tools to flag potentially risky returns for physical inspection. This intensifying competition means companies must continually innovate and upgrade services to maintain market advantages.
Recent surveys of executives by major consulting firms reveal widespread belief that generative AI will eventually transform business landscapes, though respondents remain cautious about its immediate implementation. Reports indicate that 85% of merchants participating in a Happy Returns/National Retail Federation survey use AI or machine learning to identify and combat fraud, with varying degrees of success—suggesting AI's application in return fraud detection remains developmental, requiring ongoing refinement.
The Future of Fraud Detection: AI-Human Collaboration
Happy Returns' experience demonstrates AI's significant potential in return fraud detection, though it cannot fully replace human reviewers. The most effective strategy combines AI screening with human judgment, leveraging both technologies' strengths. As fraud tactics grow more sophisticated, AI must evolve accordingly. Juan Hernandez-Campos, COO of Happy Returns, notes that as bad actors adapt, their systems must too—meaning algorithms require constant updates to counter new fraudulent methods.
Return Vision provides additional safeguards against subtle anomalies. The company reports that within its network, less than 1% of returns are flagged as high-risk by the tool, with about 10% of those ultimately confirmed as fraudulent. The average fraudulent return amounts to roughly $261. As Everlane's Green explains: "When a customer claims to return $300 boots but sends dirty old sneakers, that should be immediately obvious."
Looking ahead, as AI technology continues advancing, retailers may become more effective at identifying and preventing return fraud—reducing losses, improving profits, and delivering better shopping experiences for consumers.