Returns Fraud Detection

Returns fraud represents one of the most significant and growing financial drains on the retail industry. According to the 2024 Consumer Returns in the Retail Industry Report from Appriss Retail and Deloitte, which analyzed data from more than 60 leading U.S. retailers and surveyed 150 retail executives, fraudulent returns and claims cost retailers $103 billion in 2024, with 15.14% of all returns deemed fraudulent. Total merchandise returns for the year reached $685 billion, representing 13.21% of the $5.19 trillion in total U.S. retail sales. The problem is especially acute in ecommerce, where the return rate reached 24.5% compared with 8.7% for in-store purchases, according to the same Appriss Retail and Deloitte report.

The types of fraud are diverse and increasingly sophisticated. The 2024 Appriss Retail and Deloitte survey of 150 retail executives found that 60% reported incidents of wardrobing, 55% cited returns of items obtained through fraudulent or stolen tender, and 48% faced occurrences of returning stolen merchandise. A 2025 NRF and Happy Returns survey of 358 ecommerce professionals at large U.S. retailers found that 85% of retailers are deploying AI or machine learning to detect and prevent return fraud, though only 45% find these tools effective so far. The financial burden extends beyond the fraud itself: processing a single return costs between 20% and 65% of the item's original value according to Shopify's 2025 analysis of ecommerce returns, and an Optoro study cited by NBC News found the average processing cost reaches 30% of the original price.

Retailers face a difficult balancing act. Restrictive return policies risk alienating loyal customers, with the 2024 Appriss Retail consumer survey of more than 1,000 North American shoppers finding that 55% of consumers avoid buying from retailers with restrictive return policies. Meanwhile, a 2025 NRF and Happy Returns survey of 2,006 consumers found that 71% are less likely to shop with a retailer again after a poor return experience. This tension between fraud prevention and customer retention makes intelligent, data-driven detection essential.

AI-driven returns fraud detection operates across multiple layers of the return lifecycle, combining predictive machine learning, computer vision, and graph analytics to identify suspicious activity without imposing blanket restrictions on legitimate customers. Predictive AI models analyze each return transaction against a customer's historical purchase and return behavior, flagging anomalies such as unusual return frequency, mismatched product categories, or repeated returns of all items from an order. According to a 2025 article in VKTR featuring Appriss Retail's chief data scientist, predictive AI can review a shopper's historical data and recommend to a retail agent whether a return should be processed, while generative AI helps loss prevention teams scan databases of incident reports and connect cases linked to organized retail crime groups.

Computer vision adds a physical verification layer that addresses fraud types manual inspection cannot consistently detect. A Feb. 2026 partnership announcement between ReturnPro and Clarity described AI-powered verification technology that combines X-ray intelligence with computer vision to see inside returned products without opening the box, comparing each item against its original manufacturer profile and detecting counterfeits, component swaps, and product manipulation in 3.2 seconds. A CBS News report from Jan. 2026 documented how a UPS subsidiary's processing hub used AI image comparison to detect a pair of designer jeans that had been swapped with a cheaper duplicate and an orange sweater replaced with a version costing $36.99 instead of the original $200 item.

Graph analytics and network detection represent a third critical capability. These systems link accounts, addresses, payment methods, and return locations to uncover organized fraud rings that operate across multiple stores and states. According to Retail TouchPoints, AI was able to link store credits, gift cards, and credit cards that were part of an organized retail crime ring attempting to return $224,000 of merchandise across 215 stores nationwide. Appriss Retail documented a case in which AI-driven tender laundering models identified a coordinated fraud network in the Baltimore-Washington D.C. metro area that had caused $27,000 in losses at a national shoe retailer operating more than 500 stores.

Implementation challenges remain significant. A McKinsey study cited by VKTR in 2025 found that just 1% of companies investing in AI consider themselves AI mature. Data quality is a persistent obstacle, as AI models depend on accurate, unified transaction data across channels. False positives can damage the customer experience if legitimate returns are incorrectly flagged, and certain fraud types such as wardrobing remain difficult for current AI systems to detect, as noted by Happy Returns in a Dec. 2025 CBS News report. Retailers must also contend with an adversarial dynamic: fraudsters are now using generative AI to create fake damage photos, counterfeit receipts, and fabricated shipping documentation, according to a March 2026 PYMNTS report.

A national shoe retailer operating more than 500 stores in the United States deployed AI-driven return authorization to combat an organized retail crime network based in the Baltimore-Washington D.C. metro area, as documented by Appriss Retail in 2025. The organized crime group exploited lenient return policies through tender laundering, returning stolen items without receipts in exchange for gift cards, then purchasing new items and immediately returning them with proper receipts to obtain cash refunds. The retailer's AI system, which uses multi-layered machine learning models including a tender laundering detection algorithm, identified the circular transaction patterns and used network graph modeling to link multiple individuals exhibiting the same behaviors across stores and states. The system uncovered $27,000 in losses from this single ring and enabled the retailer to dismantle the operation.

Happy Returns, a UPS subsidiary that processes millions of returns annually for major apparel brands, launched an AI-powered visual verification pilot called Return Vision in late 2025, as reported by Fox News and CBS News. The system compares images of returned items against retailer product catalogs to identify counterfeit swaps and substitutions. In early results, less than 1% of returns flowing through the network were flagged as high risk, and of those flagged returns, approximately 10% to 13.5% were confirmed as fraudulent. In one documented case, the AI detected subtle differences in a waistline pattern on a pair of designer jeans, revealing the returned item was a cheaper duplicate rather than the $298 item originally purchased. The system's average prevented loss per confirmed case exceeded $200.

At a broader industry level, a 2025 Total Retail report documented a global apparel brand that increased revenue by more than $20 million through improved AI-based fraud detection, and a major consumer packaged goods company that reduced return policy violations by more than 50% within 60 days of deploying advanced analytics. These cases illustrate the range of outcomes achievable across different retail segments and implementation approaches.

The returns fraud detection market spans several categories of providers, from specialized return authorization platforms to broader ecommerce fraud prevention suites. The market is consolidating around omnichannel solutions that integrate online and in-store data, as illustrated by the Jan. 2025 partnership between Riskified and Appriss Retail to deliver a unified fraud and abuse prevention solution covering the entire customer journey. According to a 2025 Riskified and Opinium/Cebr study, returns, refunds, and exchanges constitute a $394 billion expense for retailers in key ecommerce markets, underscoring the scale of the addressable market.

Selection criteria for returns fraud detection solutions should include the breadth of the provider's transaction data network, the ability to integrate across point-of-sale, ecommerce, and customer service systems, the sophistication of machine learning models for behavioral analysis and identity resolution, and the availability of computer vision capabilities for physical product verification. Organizations should also evaluate false positive rates, implementation timelines, and the provider's ability to adapt to emerging fraud vectors such as AI-generated documentation. The maturity of the provider's graph analytics for detecting organized retail crime networks is an additional differentiator for large omnichannel retailers.

Providers active in returns fraud detection and adjacent commerce fraud prevention include:

• Appriss Retail -- return and claim authorization platform serving more than 60 of the top 100 U.S. retailers across 150,000 locations, with AI-driven behavioral analytics, identity linking, and organized retail crime case management
• Riskified -- ecommerce fraud and risk intelligence platform with AI-powered policy abuse prevention, dynamic returns decisioning, and chargeback guarantee model
• Signifyd -- commerce protection platform offering return abuse prevention, automated order review, and guaranteed fraud protection across major ecommerce platforms
• Forter -- real-time fraud prevention platform using behavioral analytics and a database of more than 175 million identities for policy abuse detection and account protection
• Happy Returns (UPS) -- returns logistics and processing platform with AI-powered Return Vision technology for visual product verification at physical drop-off locations
• Clarity -- item intelligence platform using X-ray imaging and computer vision to detect counterfeit, altered, and fraudulent returns at the point of return
• Yofi -- fraud and abuse prevention platform focused on detecting AI-generated fake damage claims and return manipulation across direct-to-consumer brands