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UPS's reverse logistics company, Happy Returns, is using AI-powered fraud detection called 'Return Vision' to combat return fraud during the holiday season. The technology analyzes suspicious returns and flags them for human review, potentially preventing $76.5 billion in annual retail losses in the US. Major retailers like Everlane, Revolve, and Under Armour are among the clients testing this technology.
UPS's subsidiary Happy Returns has introduced an AI-powered return fraud detection system called 'Return Vision' to address the growing issue of fraudulent returns during the holiday season. The technology is designed to identify suspicious returns by analyzing package contents and flagging them for further human review. This development comes as nearly one in ten retail returns in the United States is estimated to be fraudulent, resulting in significant financial losses for retailers.
Return fraud represents a $76.5 billion annual problem for US retailers, according to UPS's reverse logistics data. The issue has become particularly pronounced during peak shopping seasons when the volume of returns surges. Fraudulent activities include customers returning counterfeit or lower-value items instead of the original purchased products.
Happy Returns has begun testing the Return Vision system with several major retail clients, including fashion retailers Everlane, Revolve, and Under Armour. The AI system works by marking suspicious packages for detailed analysis and human verification. If fraud is confirmed, the refund can be withheld, thus protecting retailers from financial losses.
The implementation of this AI technology represents a significant step forward in the fight against return fraud. By leveraging advanced technology, retailers can better protect themselves against fraudulent activities while maintaining customer trust through legitimate return processes. The success of this system could potentially lead to wider adoption across the retail industry, particularly during peak shopping seasons.
AI Implementation for Fraud Detection
Return Fraud Mitigation Measures