The findings are based on survey responses from more than 1,000 consumers and analysis of returns data linked to 250 million unique customer identifiers, with the study integrating shrink, fraud, returns abuse, and operational leakage into a single enterprise-wide assessment.
According to the report, merchandise returns accounted for $706bn of the losses last year. Of these returns, preventable loss from fraud and abuse reached $100bn, representing 14.2% of all returns.
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Within the category of returns-related loss, returns abuse comprised 12%, while fraud was responsible for just 2%.
Shrinkage contributed an additional $90bn in losses. The report attributes preventable shrink mainly to employee theft amounting to $26bn, inventory errors resulting in $19bn, operational errors accounting for $12bn, and organised retail crime causing $9bn in losses.
Appriss Retail CEO Michael Osborne said: “Returns overwhelmingly power the majority of financial loss that retailers endure. Every dollar lost to returns is a dollar straight off the bottom line. To stop the bleeding, leaders must look at returns, fraud, and shrink through the lens of Total Retail Loss, build a system of collaboration, and implement cross-functional muscle. Retailers that continue to work in silos will continue to erode profits.”
The 2026 Total Retail Loss Benchmark Report also details how fragmented data costs businesses billions each year. Cross-channel fraud from buy online, return in-store (BORIS) transactions resulted in a $4bn loss.
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By GlobalDataReturn channels contributed differently to the overall total. Buy in-store, return in-store transactions generated $367bn, representing 52% of returns.
Buy online, return in-store transactions accounted for $208bn, or 29%, while buy online, return online transactions made up $131bn, or 19%.
The report analyses consumer preferences for returning goods and explores factors influencing customer loyalty, as well as attitudes towards the use of AI in the returns process.
It finds that when applied with precision, data analytics and AI can identify and warn customers who show patterns of abuse without losing them entirely.
The data reveals that 90% of consumers are willing to purchase again after receiving a warning, which saves retailers $75bn in retained revenue.
In addition, 80% of surveyed consumers want transparency in how AI makes return decisions and 71% trust human associates more than AI for approvals. Only 10% trust AI outright.
Furthermore, the report showed that retailers who centralise omnichannel data and maintain clear communication can deploy AI tools more effectively to discern abusive patterns, warn repeat offenders, and sanction genuine returns without negatively impacting loyal customers.
