The Refunds Abuse Dilemma: Gaining Visibility Into Your Customers

Refunds abuse is on the rise, but existing fraud prevention strategies simply weren’t designed with this challenge in mind. When it comes to this kind of policy abuse, looking at one order is not enough to make an accurate decision. In this session we’ll look at methods to efficiently take on this growing challenge, take a deep dive into Machine Learning cluster technology’s ability to look at all customer interactions, at the same time, and evaluate commonalities in behavior, not just anomalies.

Learning Objectives:

• Detecting refund abuse and flagging or denying abusive refund claims (INR, missing, damaged, etc.), flagging checkouts as high risk for abuse, and blocking or applying friction to serial abusers at checkout.
• Policy abuse in cross-border purchases and why it's more complicated than domestic purchases.

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