Return Fraud Detection

Return Fraud Detection uses AI to flag abusive and fraudulent returns, enabling tighter returns control, by detecting suspicious patterns in return, purchase, and device data, across returns management and refund review.

Returns policies are exploited through wardrobing, receipt fraud, and serial abusive returns that blend into legitimate volume. Manual review cannot keep pace at retail scale, so fraudulent returns erode margin and inflate reverse-logistics cost.

The AI runs detection across return requests, purchase history, device signals, and policy data, flagging suspicious returns for hold or manual review while letting genuine returns flow through.

Lowers the fraudulent return rate and reduces margin lost to abusive returns, without raising friction for legitimate customers.

• •Historical return requests, purchase history, device signals, and policy data are available with stable identifiers and sufficient coverage for the target workflow.
• •Source systems for returns management and refund review workflows expose the required records through a repeatable export or service interface.
• •A named business owner exists to review flagged suspicious return requests and confirm the action workflow.