Notifications
Clear all
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.
NHIMG editorial — based on content published by Riskified: Refunduary is here: How to prevent refund policy abuse, protect profitability, and optimize CX
By the numbers:
- Orders placed during November and December drive 31% of all refund claims across the year.
- More than 55% of January claims stem from pre-holiday purchases.
- Approximately 1-2% of total order value is refunded annually, and nearly a quarter of refunded dollars come from abusive claims.
Questions worth separating out
Q: How should retailers reduce refund abuse during peak season?
A: Retailers should combine identity-linked evidence, claim risk scoring, and exception review for refund decisions during high-volume periods.
Q: Why do refund fraud losses rise after the holiday rush?
A: Refund fraud rises after the holiday rush because merchants are processing more claims, more exceptions, and more customer contacts at once.
Q: What do retailers get wrong about refund abuse controls?
A: Many retailers focus on blanket refund rules instead of claim-specific evidence and operational context.
Practitioner guidance
- Implement identity-linked claim validation Require refund decisions to consider order history, fulfilment evidence, account age, and prior claim behaviour before approval.
- Separate service goals from refund authority Keep customer service metrics focused on response time, but route high-risk refund approvals through fraud or operations review so queue pressure does not override control quality.
- Use season-aware risk scoring Raise review thresholds automatically during Q4 and the January return wave, when claim volume and operational strain make default approvals more likely.
What's in the full report
Riskified's full report covers the operational detail this post intentionally leaves for the source:
- Claim-type breakdowns that separate Item Not Received from other refund abuse patterns
- Operational indicators used to decide when a refund claim should move from default approval to manual review
- Seasonal timing analysis showing how post-holiday volume changes claim behaviour
- Context for retailers trying to tune fraud thresholds without adding unnecessary friction
👉 Read Riskified’s analysis of post-holiday refund abuse and peak-season claims risk →
Refund abuse after peak season: what retailers need to change?
Explore further
11/07/2026 2:07 pm
Refund abuse is an identity and entitlement problem, not just a customer service nuisance. Claims fraud works because merchants must decide whether the claimant is entitled to value, yet many operating models treat that decision as a lightweight service transaction. When fulfilment, account, and purchase history are not evaluated together, abusive claims can pass as routine exceptions. Practitioners should treat refund governance as a controlled entitlement process.
A question worth separating out:
Q: How do you know if refund governance is working?
A: Refund governance is working when abusive claims fall without increasing legitimate customer friction. The clearest signals are a lower share of high-risk refunds, fewer default approvals under backlog pressure, and more consistent review decisions across peak and off-peak periods.
👉 Read our full editorial: Refund abuse peaks after the holidays, exposing policy gaps
