How do retailers use AI to detect return fraud?

A Costly Problem Hiding in Ordinary Return Data

Return fraud and abuse — ranging from returning used items as new to exploiting return policies for financial gain — represents a persistent cost for retailers, one that’s difficult to address through blanket policy changes without also inconveniencing honest customers. AI-based fraud detection has become a common tool for tackling this problem more precisely, aiming to identify likely abuse based on patterns in behavior rather than applying the same scrutiny to every return regardless of risk.

The core idea is to let the vast majority of legitimate, low-risk returns proceed smoothly while directing closer attention specifically toward transactions that look statistically unusual.

What the Models Actually Look For

AI fraud detection systems are typically trained on large volumes of historical return data, learning what patterns are associated with legitimate returns versus those historically linked to fraud or abuse. Common signals include the frequency of returns relative to purchases for a given account, patterns suggesting an item shows signs of use before being returned as unused, inconsistencies in receipt or order information, and behavioral patterns that resemble recognized abuse tactics, such as buying an item for a single use and then returning it. Rather than relying on any single signal in isolation, these models typically weigh many factors together to generate an overall risk assessment for a given return.

Because return behavior differs across product categories — electronics, apparel, and furniture each have different typical return rates and reasons — models are often tuned separately by category to avoid misapplying patterns from one type of product to another.

Human Review as a Safeguard Against False Flags

Because legitimate shoppers can occasionally exhibit unusual but entirely honest return patterns — for example, someone who orders multiple sizes intending to return the ones that don’t fit, a common and generally accepted practice — most retailers build in a human review step for flagged transactions rather than automatically denying them. This helps reduce the risk of penalizing genuine customers while still directing scrutiny toward the transactions most likely to represent actual abuse. Some retailers also participate in third-party return verification networks that pool anonymized return behavior data across multiple retailers, which can help identify patterns of abuse spanning several stores that wouldn’t be visible to any single retailer working from its own data alone.

Because thresholds and practices vary significantly between retailers, what triggers a flag at one store may not at another, reflecting differences in risk tolerance, product mix, and the sophistication of a given retailer’s fraud detection system.

Bottom Line

Retailers use AI to detect return fraud by analyzing patterns across a shopper’s return history and comparing them against known indicators of abuse, flagging unusual transactions for further human review rather than automatic denial. This approach aims to target actual bad-faith behavior while minimizing friction for the large majority of honest returning customers.