Topic starter
11/07/2026 1:56 pm
TL;DR: November and December orders drive 31% of annual refund claims, while more than 55% of January claims come from pre-holiday purchases, showing how peak-season volume masks Item Not Received and Did Not Arrive abuse, according to Riskified. Static refund policies and manual backlogs are not enough when fraudsters time claims to overwhelmed operations.
Editorial analysis by NHI Mgmt Group, based on content published by Riskified: “What predicts refund abuse? Get the data for retailers”.
By the numbers:
- November and December orders drive 31% of all refund claims across the year.
Key questions
Q: What breaks when refund policy controls are static during peak season?
A: Static refund controls break when operational pressure changes how decisions are made.
Q: Why do Item Not Received claims create higher fraud risk after the holidays?
A: Item Not Received claims become riskier after the holidays because merchants are handling more orders, more returns, and more delivery exceptions at once.
Q: How should retailers decide when to automate refund approvals?
A: Retailers should automate refund approvals only when the claim type, claimant history, and delivery evidence all fit a low-risk pattern.
Practitioner guidance
- Bind refunds to fulfilment evidence Require delivery status, order history, and claim context to reconcile before automated or manual refund approval.
- Segment refund policies by claim type and season Use different thresholds for Item Not Received, Did Not Arrive, and exchange-related claims, especially during January backlog periods.
- Introduce claimant risk scoring Weight account age, repeat-claim behaviour, device consistency, and claim timing so support teams see risk before approving a payout.
Bottom line: Post-holiday refund abuse is not just a fraud spike. It is the point where seasonal load exposes weak decision controls and fragmented evidence.
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11/07/2026 2:07 pm
Refund abuse is a policy-governance failure before it is a fraud event. The article shows that claim handling breaks when static rules meet peak-season workload and cross-system evidence gaps. That means the real failure is not just abuse volume, but the absence of a control model that ties fulfilment evidence, claim context, and approval authority into one governed decision. Practitioners should treat refund governance as a lifecycle control, not a back-office exception process.
A question worth separating out:
Q: What should fraud teams measure to spot refund abuse early?
A: Fraud teams should measure claim timing, claim type concentration, approval rates under backlog conditions, and repeat-claim behaviour across customer accounts. If approval rates rise as queues grow, the control is no longer functioning as designed. Those signals show whether refund governance is adapting to seasonal pressure or quietly degrading.
👉 Read our full editorial: Refund abuse peaks after the holidays, exposing policy gaps