Smarter Reverse Logistics Across the UPS Network
A logistics company affiliated with UPS is rolling out artificial intelligence to curb return fraud during the busiest shopping months. Happy Returns, a UPS subsidiary, has launched a system called Return Vision that examines each return in real time to spot unusual behavior before items flow back into inventory. The push responds to rising losses that accompany the year-end sales peak, a problem that already weighs on retail balance sheets. Recent estimates put annual U.S. returns near 849.9 billion dollars, with roughly 9 percent attributed to fraudulent practices, which could translate into about 76.5 billion dollars in 2025.
The system blends automated analysis with human oversight. When a shopper starts a return online or visits one of the network’s Return Bars, the platform analyzes data and visual evidence for the item. The model checks labels, serial numbers, descriptions and photos, then compares that information with historical patterns to estimate the likelihood of irregularities. Cases with higher risk are routed to an audit track where specialized teams perform manual verification at Happy Returns processing centers in California, Pennsylvania and Mississippi. The goal is not to slow the overall flow, but to focus scrutiny where there are clear signals. In testing, fewer than 1 percent of returns were flagged as potentially problematic, and about 10 percent of that subset was confirmed as fraud, with an average case value of 261 dollars.
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The rollout leverages UPS’s reverse-logistics footprint. Happy Returns operates roughly 8,000 no-box, no-label drop-off points, integrated across partner retailers and The UPS Store network. That reach improves the customer experience while raising the quality of captured data. Returns processed in store make it possible to record images and metadata at the moment of scan, reducing errors and speeding refund decisions. The company says the use of AI is meant to protect retailers without adding friction for legitimate customers, a delicate balance in a category where convenience directly influences purchase decisions.
Fashion and sports brands such as Everlane, Revolve and Under Armour are among the retailers participating in the pilot. For online-heavy businesses, the challenge is twofold. Practices like wardrobing, where a customer uses an item before returning it, call for finer controls without damaging the relationship with the shopper. At the same time, label tampering or item swapping during the return process creates costs that multiply with daily parcel volume. Artificial intelligence does not eliminate such behavior, but it does separate higher-risk cases from the standard stream and assigns review resources where they are most valuable.
The retail backdrop underscores the urgency. Easy returns have become a baseline expectation, with no-packaging processes and fast refunds as standard. That convenience, which boosts conversion and loyalty, also expands the attack surface for fraud. Bracketing, the practice of buying multiple sizes or variants with the intention of returning part of the order, adds to the volume that must be handled quickly and accurately. Without automation and intelligent prioritization, the operating system strains.
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From an operational standpoint, data quality at the capture point is decisive. Return Vision detects discrepancies between what the customer reports and the actual state of the received item, validates labels and codes, and picks up subtle signs of manipulation. When a case goes to manual review, auditors follow uniform criteria to decide whether to refund, liquidate the item or escalate with the retailer. The traceability of each decision matters as much as model accuracy, especially during peak season when companies must justify every hold or denial.
The numbers help size the problem. With returns representing about 15.8 percent of annual sales and persistent pressure on margins, the return on investing in fraud detection shows up as fewer improper refunds and faster re-stocking of valid products. If the tool narrows fraud without penalizing legitimate customers, the impact is visible in quarterly results. The combination of frontline AI models and human review is aimed at striking that balance.
The sector is also clear about the limits. Not every questionable practice is easy to classify, such as sending back used items that appear to be in good condition. Effectiveness depends on the quality and consistency of data supplied by retailers and logistics operators. That is why shared rules, continuous updates to training data and coordination with legal and customer service are as important as the algorithm itself.
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This case points to a broader need for specialized teams that can connect models to existing systems, ensure privacy compliance and keep operations steady during demand spikes without hurting service. Square Codex, headquartered in Costa Rica, operates in that intersection. Through a nearshore staff-augmentation model, it integrates software engineers, data specialists and AI professionals into North American teams, with the goal of pushing AI initiatives beyond the pilot stage and turning them into stable, measurable operations.
Square Codex extends that reach with nearshore pods that embed directly with retailer and logistics squads. Its teams build data pipelines with clear contracts and lineage, implement CI/CD and MLOps, and design privacy controls and role-based access. In fraud and returns, they develop risk features, orchestrate A/B tests and set up observability to detect model drift and behavioral shifts. They also connect order management, warehouse systems and payment gateways to align return decisions with refunds and real-time inventory. The result is an auditable, scalable flow that is oriented toward concrete outcomes.