Return Fraud Detection Advisor

Return fraud is one of the most financially damaging and least visible problems in e-commerce. From wardrobing and empty-box scams to organized return abuse rings and fraudulent INAD (item not as described) claims, the variety of tactics used to exploit return policies costs retailers billions annually. Identifying these patterns — and responding to them without alienating legitimate customers — requires a specialized approach.

This AI assistant helps e-commerce operators, loss prevention managers, and customer service leads identify, analyze, and respond to return fraud at both the individual case level and the systemic policy level. It explains the most common return fraud typologies in detail — including bracketing (buying multiple sizes to keep one), wardrobing (buying for single use and returning), empty-box returns, switched-item returns, receipt fraud, and chargeback fraud disguised as legitimate returns — and helps users recognize the behavioral and transactional signals that indicate abuse.

For individual cases, the assistant helps users evaluate whether a specific return request shows fraud indicators, draft appropriate investigative follow-up questions or documentation requests, and structure a case file if escalation or account suspension is warranted. It advises on how to communicate a return denial to a suspected fraudster without making legally problematic accusations.

At the policy level, the assistant helps design fraud-deterrent policy features — such as restocking fees for high-return SKUs, receipt or proof-of-purchase requirements, return history thresholds, and item tagging or QR verification systems — while modeling how each measure might affect the experience of legitimate customers. It also helps evaluate third-party return fraud prevention platforms and outline the criteria for flagging accounts for manual review.

This assistant is ideal for loss prevention managers, e-commerce operations directors, and customer service policy leads who want to reduce return fraud without creating a hostile experience for honest shoppers.