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12/07/2026 12:26 pm
TL;DR: Refund abuse now sits at the intersection of fraud, customer experience, and AI-assisted service workflows, with Riskified citing MRC’s view that it is the top merchant fraud threat for 2025 and a 2025 survey showing customers increasingly accept AI for order-status and returns tasks. The control problem is no longer just detection speed but whether review, approval, and audit decisions can keep pace without weakening trust.
NHIMG editorial — based on content published by Riskified: Enhancing customer experience while combating refund fraud
By the numbers:
- Refund abuse was flagged by MRC as the top fraud threat for merchants in 2025.
- A 2025 Riskified global consumer survey found that some customers prefer using AI-enabled options for tasks such as checking order status and initiating returns.
Questions worth separating out
Q: What breaks when refund decisions rely on simple rules like address matching?
A: Simple rules create blind spots because fraudsters can vary shipping details, split claims across identities, or mimic normal customer behaviour.
Q: Why do refund abuse controls matter for customer experience as well as fraud reduction?
A: Refund controls shape the speed, clarity, and fairness of the service experience.
Q: What do merchants get wrong about using AI in returns and refunds?
A: They often treat AI as a speed layer rather than a governed decision layer.
Practitioner guidance
- Define refund decision thresholds Set clear thresholds for auto-approve, auto-reject, and manual review based on claim type, customer history, and signal confidence.
- Add layered abuse signals Use more than address matching to evaluate claims.
- Require auditable claim trails Log the signals, policy outcome, and human override for every refund and return decision.
What's in the full article
Riskified's full article covers the operational detail this post intentionally leaves for the source:
- Practical guidance on how fraud and CX teams can share claim signals without slowing response times.
- Examples of deeper abuse signals that go beyond simple address-linking logic.
- Operational ideas for automating low-risk refunds while preserving human review for ambiguous claims.
- A customer-experience focused guide for balancing tighter controls with faster resolution during peak return periods.
👉 Read Riskified's guide on enhancing customer experience while combating refund fraud →
Refund abuse and AI-assisted claims: what should CX teams change?
Explore further
12/07/2026 12:40 pm
Refund abuse is becoming a governance problem, not just a fraud pattern. Merchants are no longer dealing only with isolated bad claims. They are managing a decision system that affects revenue, customer trust, and auditability at the same time. That puts claims handling in the same control conversation as identity verification and access governance, because every approval depends on confidence in the requester and the context. The practitioner conclusion is that refund policy now needs explicit governance, not just operational tuning.
A question worth separating out:
Q: Who is accountable when a refund workflow is abused at scale?
A: Accountability sits across fraud, CX, finance, and operations, because all of those teams influence the decision surface. Fraud owns abuse detection, CX owns service quality, and leadership owns the policy trade-off. The practical answer is to assign one accountable owner for claim governance and require traceable decision records.
👉 Read our full editorial: Refund abuse is reshaping customer experience and fraud controls
