Returns & Refunds Management

Returns management has become one of the most complex operational challenges in modern commerce. Return rates in online fashion retail average between 30% and 40%. This unprecedented volume of reverse logistics strains warehouse capacity creates bottlenecks in customer service and erodes profi t margins through processing costs and lost sales.

The fi nancial burden extends well beyond shipping and handling. Fraudulent returns cost retailers more than $100 billion in 2024, with 15% of all returns classifi ed as fraudulent, according to industry research. Common schemes include “wardrobing,” in which consumers buy an item, for example a fancy dress, use it, and then return it—a problem reported by 60% of retailers.

The complexity of returns management creates friction across departments, requiring coordination among warehouse operations, quality inspection, and accounting—each introducing potential delays. Environmental concerns compound the issue, as many returned items end up in landfi lls.

Omnichannel commerce adds another layer of complexity: Ecommerce returns account for more than 52% of retail returns, according to a 2024 study by Deloitte and Appriss Retail. More than 30% of consumers in a 2025 study by Digital Commerce 360 and Bizrate Insights reported having returned an online order to a retail store or a mall-based return facility, forcing retailers’ physical locations and return providers to handle returned online merchandise. 159 2.3 Fulfill (Supply Chain & Logistics)

Modern AI–driven returns management systems integrate multiple technologies to automate and streamline traditionally manual processes. Machine learning algorithms analyze historical transactions and customer behavior to predict return likelihood and identify fraud in real time. Predictive AI models examine factors such as repeated full-order returns or purchases with multiple delivery addresses to flag suspicious behavior before refund processing begins.

Computer vision technology has also become central to return processing. AI-powered image analysis detects defects or inconsistencies in color, stitching, or labeling. This allows for faster and more consistent inspections while reducing human error. Many systems now allow customers to upload images through self-service portals so that AI can verify product condition and eligibility before items are shipped back, lowering both cost and carbon footprint.

Successful implementation requires tight integration across enterprise systems. Machine learning models connect with ERP, WMS, and CRM software to continuously analyze return patterns and determine optimal routing. Returned products may be sent back to primary warehouses, redirected to secondary markets, or moved into liquidation channels. Natural language processing further enhances customer communication by automatically categorizing return reasons and extracting insights from free-text feedback.

Challenges remain. Data quality is critical—AI models need large, clean datasets to perform effectively. Edge cases such as customized or perishable goods still require human oversight. Privacy concerns also persist as systems collect behavioral data for fraud detection, underscoring the importance of strong data governance and compliance frameworks.

Several global companies have demonstrated the value of AI in returns management. Nike’s AI tool, Nike Fit, uses computer vision and machine learning to scan a customer’s feet and recommend accurate shoe sizes, cutting sizing- related returns by up to 60%. Since size issues represent one-third of online footwear returns, the impact on logistics and profitability has been significant.

Beauty brand Estée Lauder reported a substantial lift in conversions after implementing Perfect Corp.’s AI-powered virtual try-on technology, which allows shoppers to visualize products before purchasing, reducing returns, and improving satisfaction. Fast-fashion retailer H&M deployed a fashion AI supply chain management system that analyzes both sales and return data to optimize inventory distribution across its more than 5,000 global stores.

AI fraud detection tools are also gaining traction beyond retail. Mastercard says its generative AI–based fraud detection system reduced false positives by up to 200% while increasing fraud-detection speed by 300%.

The returns management technology market has matured into a diverse ecosystem of specialized vendors. Providers range from comprehensive returns management platforms to fraud detection, computer vision, and system integration specialists.

Organizations selecting solutions should weigh integration flexibility, scalability, and alignment with industry- specific needs. Comprehensive platforms are best suited for retailers with high returns volume, while modular tools may be more appropriate for mid-market companies or distributors. Global coverage, logistics partnerships, and strong data privacy standards are essential for maintaining both compliance and customer trust. Fraud detection accuracy is particularly important, as even small model errors can significantly affect profitability and customer satisfaction.