AI Returns Processing in Retail

Retail returns contain useful information about fit, quality, shipping, packaging, listing accuracy, and customer expectations. A system may read a return reason, classify an item’s condition from a photo, or suggest whether it should go to resale, repair, or disposal. These tasks are different from deciding whether a customer is entitled to a refund. The decision depends on the transaction, the seller’s disclosed policy, the item, and applicable consumer rules. Keep the policy version and order facts available to the reviewer. AI can summarize an explanation, but should not invent a return deadline or silently change a policy. An image can miss hidden damage or show packaging from another item. A risk score may reflect legitimate behavior such as frequent clothing returns or gifts, not fraud. Treat flags as a reason to inspect evidence, give staff an override, and provide a way to correct an order or identity mismatch. Measure classification accuracy by product type and return reason. Separate operational routing from customer-facing eligibility decisions. Track false fraud flags, delayed resolutions, reopened cases, and items incorrectly sent to waste. Reduce data exposure: images may contain labels, addresses, or payment slips. Limit retention and access to what the process needs. The goal is to handle products and customer requests accurately while learning from recurring return causes, not to maximize the number of claims denied.