International Business Machines Corporation
DYNAMIC RETURN OPTIMIZATION FOR LOSS PREVENTION BASED ON CUSTOMER RETURN PATTERNS

Abstract:

Approaches presented herein enable dynamically determining a validity of a return. More specifically, a system obtains a return request from a customer, a transaction history of the customer, and a set of return policy rules. A generative adversarial network (GAN) trained to detect non-genuine returns is applied to the return request. The GAN uses, among other this, the transaction history of the customer and the set of return policy rules as parameters of the GAN. Based on an output of the GAN, at least one return processing action is recommended and implemented.