Generative AI tools are making refund fraud dramatically easier and more scalable for ecommerce criminals. U.S. retailers processed approximately $849.9 billion in merchandise returns in 2025, of which some 9% were fraudulent (Practical Ecommerce). Ecommerce return rates are particularly vulnerable, reaching 19.3% compared to brick-and-mortar rates, creating a larger target for fraud.
AI-generated refund fraud extends beyond simple fake photos. Fraudsters can now fabricate cracks, stains, mold, tears, leaks, dents, and missing parts in products; damaged packaging or crushed shipping boxes; product colors or features that supposedly differ from listings; customer-service chats suggesting merchant approval; shipping records and carrier documents; and written complaints tailored to merchant return policies (Practical Ecommerce). Real-world cases have already emerged: retailers Bogg Bag and Boll & Branch have each encountered AI-falsified refund proof (Practical Ecommerce). The core vulnerability is that online merchants typically evaluate refund claims without physically inspecting merchandise, relying instead on photos, descriptions, and delivery information.
Merchants can deploy countermeasures including image metadata analysis, reverse-image searches, manual reviews of high-value claims, requiring returned products for selected items, and AI-powered image screening. However, each control carries costs and limitations: detection tools produce false positives and become less reliable as image generators improve, while stringent policies increase return shipping, inspection, and support costs (Practical Ecommerce). The fundamental challenge for commerce practitioners is the asymmetry—fraudsters can create convincing synthetic claims in minutes, while merchants must invest staff, systems, and carrier coordination to challenge them.