U.S. retailers are turning to artificial intelligence as return fraud reaches unprecedented levels, threatening profitability at a time when margins are already strained by high logistics and labor costs. Nearly one in ten returned items is now fraudulent, according to industry estimates, pushing total annual losses from return abuse to more than $76 billion. Against this backdrop, UPS-owned Happy Returns is rolling out an AI-powered fraud detection system during the peak holiday return season, signaling how advanced analytics are moving from theory into frontline retail operations.

Holiday Returns Expose a Growing Structural Weakness

Return fraud has quietly become one of the most expensive leakages in U.S. retail. As e-commerce has normalized frictionless refunds and instant credits, opportunistic abuse has followed. Customers increasingly initiate returns but send back lower-value substitutes or counterfeit items that cannot be resold. With total retail returns projected to reach nearly $850 billion in 2025—roughly 16% of all sales—even small fraud rates translate into material financial damage.

This problem is especially acute during the holidays, when return volumes spike and operational bottlenecks make manual inspection impractical. Retailers face a “double cost” dynamic: absorbing logistics and processing expenses while also losing the original inventory. In an environment where consumers remain price-sensitive and competition is intense, these losses weigh directly on earnings quality.

How AI Is Being Embedded Into Reverse Logistics

Happy Returns’ new AI system, branded Return Vision, is designed to detect fraud throughout the lifecycle of a return. The tool begins working when a shopper initiates a return online, flagging anomalies such as unusually fast return requests, linked email accounts, or patterns associated with prior suspicious behavior. At physical drop-off points, staff can compare items against reference images, while the AI focuses on subtler mismatches that humans may overlook.

Once consolidated shipments reach processing hubs, flagged items undergo further scrutiny. Images captured during inspection are analyzed by the system and reviewed by human auditors, blending automation with judgment. This layered approach reflects a growing consensus that AI works best as an augmentation tool rather than a full replacement for human oversight, particularly in edge cases where false positives can damage customer relationships.

Retailers Balance Convenience With Control

Boxless returns have become popular precisely because they reduce friction for consumers, often offering instant refunds. However, that convenience has also lowered barriers for fraud. Happy Returns’ challenge—and opportunity—is to preserve the speed and simplicity that shoppers expect while quietly inserting safeguards that retailers need.

Early data suggests the system is selective rather than blunt: fewer than 1% of returns are flagged as high-risk, and only a fraction of those are confirmed as fraud. While the numbers may appear small, the average fraud value of around $260 per incident makes the economics compelling at scale. For brands like Everlane, where online returns are heavily concentrated in these networks, the financial impact is already measurable.

AI Adoption Reflects a Defensive Shift in Retail Strategy

The deployment of Return Vision highlights a broader recalibration underway across retail. While executives remain enthusiastic about AI’s long-term transformative potential, current use cases are increasingly pragmatic, focused on cost containment, risk management, and operational resilience. Fraud prevention, unlike more speculative AI applications, delivers immediate and quantifiable returns on investment.

As fraudsters adapt, retailers are betting that machine learning systems can evolve faster than human-only processes. The arms race between bad actors and detection tools is unlikely to end, but AI may tilt the balance just enough to protect margins during a period when retailers can least afford leakage.