Notifications
Clear all
Topic starter
11/07/2026 8:33 pm
TL;DR: Refund abuse increasingly shows up after fulfilment, where frontline agents must decide quickly using incomplete customer data, while serial abusers exploit rigid policies and weak trust signals. Riskified’s analysis says identity- and behavior-based automation can reduce delays, false positives, and losses without turning every dispute into manual guesswork.
NHIMG editorial — based on content published by Riskified: refund fraud, customer experience, and identity-based automation
By the numbers:
- Another Riskified merchant cut its average claim response time from nine days to one, while boosting repurchases by customers filing refund claims by 132%.
- The share of orders coming from new customers increases dramatically during the end-of-year holidays by as much as 35% in sectors like fast fashion.
Questions worth separating out
Q: How should security teams handle trust decisions in refund fraud workflows?
A: Security and fraud teams should base refund decisions on multiple identity and behavioral signals rather than a single account attribute.
Q: Why do rigid refund rules create fraud and CX risk?
A: Rigid rules are easy to learn and exploit because serial abusers can tailor claims to fit known thresholds.
Q: What do organisations get wrong about new-customer trust signals?
A: They often assume an address or email is enough to establish trust, but those attributes do not tell you whether the customer is low risk.
Practitioner guidance
- Instrument refund decisions with identity and behavior signals Feed prior claims, purchase history, device consistency, and network relationships into the refund workflow so agents can see a risk score before making a decision.
- Replace rigid refund thresholds with tiered policy paths Use low-friction approval for low-risk cases, conditional holds for uncertain cases, and deeper review for repeated or high-confidence abuse.
- Give frontline teams a governed trust view Expose a concise risk summary in the agent console so the representative can see why a claim is being approved, deferred, or denied.
What's in the full article
Riskified's full article covers the operational detail this post intentionally leaves for the source:
- Worked examples of how identity- and behavior-based automation changes refund handling in customer service workflows.
- Specific performance outcomes reported by merchants, including response-time reduction and repurchase lift.
- Guidance on using network-based identity assessment for new customers with limited history.
- The practical balance between quick approvals, conditional holds, and denials in high-volume support queues.
👉 Read Riskified's analysis of refund fraud, identity signals, and CX decisioning →
Refund abuse and identity signals: what should CX teams do?
Explore further
11/07/2026 9:01 pm
Refund abuse is an identity governance problem disguised as a customer service issue. The article shows that agents are being asked to make trust decisions with incomplete evidence, which means the real control weakness sits upstream of the interaction. When identity confidence is low, policy abuse becomes easier and good customers are more likely to be caught in the same net. For practitioners, the lesson is to treat refund decisioning as a governed identity workflow, not just an operational queue.
A question worth separating out:
Q: Who should own decisions when refund fraud controls affect customer experience?
A: Accountability should be shared between fraud, CX, and identity governance teams, but one team should own the policy logic and evidence standards. The critical question is whether decisions are explainable, logged, and consistent across channels. If they are not, the organisation will struggle to defend declines, prevent abuse, and preserve customer trust.
👉 Read our full editorial: Refund abuse is an identity trust problem, not just a CX issue
