Topic starter
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.
Editorial analysis by NHI Mgmt Group, based on content published by Riskified: “The unsung heroes of ecommerce”.
Key questions
Q: What fails when refund abuse controls rely on shallow matching rules?
A: Shallow matching fails when a control assumes one visible attribute, such as shipping address, is enough to identify abuse.
Q: Why does refund abuse create both CX and fraud risk?
A: Refund abuse creates dual risk because the same decision affects customer trust, operating cost, and loss prevention.
Q: What are the signs that refund automation is failing?
A: Common signs include high manual override rates, inconsistent approvals across agents, long queue times for routine claims, and decisions that cannot be explained after the fact.
Practitioner guidance
- Strengthen refund claim scoring Use multiple behavioural and transaction signals instead of single-attribute rules such as address matching, especially where customers can easily vary the visible data.
- Require explainable auto-decisioning Document the policy conditions, evidence inputs, and exception paths behind auto-approve and auto-reject outcomes so reviewers can challenge and tune them.
- Preserve refund decision provenance Log who reviewed the claim, what data was used, and why the final decision was made so audit and appeal processes remain defensible.
Bottom line: Refund abuse is exposing the limits of manual review and simplistic rules in customer-facing refund workflows.
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12/07/2026 12:40 pm
Refund abuse is now a governance problem, not just a fraud problem. Once a merchant treats claims handling as a service-only workflow, abuse patterns can exploit the gap between customer empathy and control discipline. The real issue is not whether refunds should be faster, but whether the control model can distinguish legitimate friction from engineered misuse without degrading the customer experience.
A question worth separating out:
Q: How should teams govern AI-assisted refund decisions across fraud and CX?
A: Teams should define which claims can be auto-resolved, which require human review, and what evidence must be retained for every outcome. The key is to make reasons, audit trails, and exception handling part of the workflow, not an afterthought. That keeps automation fast while preserving accountability.
👉 Read our full editorial: Refund abuse is reshaping customer experience and fraud controls