How should merchants reduce false SNAD and INR claims without making the refund experience harder for good customers?

Merchants should segment refund and dispute handling by risk, not treat every claim the same. Clear product descriptions, timely delivery updates, accessible support, and transparent return policies reduce frustration-driven disputes. For higher-risk cases, require stronger proof such as tracking, signatures, or return inspection before releasing value. The goal is tailored friction, so honest customers move quickly while suspicious claims face verification.

Why This Matters for Security Teams

False SNAD and INR claims sit at the intersection of payments fraud, customer experience, and operational control. Merchants that handle every dispute the same way often create two failure modes at once: weak cases are accepted too quickly, and legitimate customers are forced through needless friction. The better model is risk-based handling, where evidence requirements, refund timing, and review depth change with transaction context, delivery confidence, and account history. That approach aligns with the broader identity principle behind NIST SP 800-63 Digital Identity Guidelines: assurance should increase when the risk increases, not for every interaction by default.

The practical objective is to separate genuine service recovery from abuse without turning the refund path into a maze. Teams often focus on dispute losses alone, but the real cost also includes chargeback processing, manual review load, support escalation, and churn from good customers who feel accused. Clear, consistent policy language matters as much as technical controls because ambiguity invites gaming. In practice, many merchants discover the weakness only after a pattern of repeat claims has already been normalised by a lenient refund workflow.

How It Works in Practice

A workable model starts with triage. Every SNAD or INR request should be scored using signals that are already available to the merchant: order value, customer tenure, shipping destination, delivery method, prior claim frequency, payment method, item type, and whether the customer has engaged support before filing the claim. That score should not make an automatic decision by itself, but it should determine how much evidence is needed and whether the case can be resolved instantly or routed for review.

Operationally, merchants usually get the best results when they define three handling paths:

• Low risk: fast refund or replacement, minimal delay, no extra proof beyond basic order matching.
• Medium risk: support verification, confirmation of delivery or non-receipt, and a short review window.
• High risk: stronger proof such as tracking, signature capture, delivery photo, return inspection, or buyer follow-up before value is released.

That structure reduces false claims without making all customers pay the same friction cost. It also helps support agents stay consistent, which is important because discretionary handling often creates uneven decisions that fraudsters learn to exploit. Merchants should also tighten upstream controls: accurate item titles, precise size and condition disclosures, proactive delivery alerts, and easy access to order history all reduce the ambiguity that leads to disputes. For digital delivery or marketplace sales, current guidance suggests extra attention to proof-of-access, download logs, and device or account linkage, but there is no universal standard for this yet.

Policy design should be paired with logging and feedback loops. If a claim is denied, the reason should be recorded in a structured way so analysts can see whether patterns point to a product issue, a carrier issue, or abuse. These controls tend to break down when fulfilment data is fragmented across carriers, marketplaces, and support tools because the review team cannot assemble a reliable timeline quickly enough.

Common Variations and Edge Cases

Tighter refund controls often increase service burden, requiring organisations to balance abuse prevention against customer effort. That tradeoff becomes sharper in edge cases where the evidence is inherently weak, such as low-cost items, digital goods, subscriptions, doorstep deliveries with no signature, or international shipments with inconsistent carrier tracking. In those environments, forcing heavy proof can cost more than the claim itself and can damage trust with honest customers.

There are also behavioural edge cases. First-time buyers with legitimate problems may look similar to fraud, while repeat claimants may include both abusive and highly dissatisfied customers. Best practice is evolving here: some merchants use reputation signals and case history to adjust friction, but there is no universal standard for how much history is enough to justify stricter handling. The key is to avoid blanket rules that punish entire customer segments for the behaviour of a few.

Another common mistake is treating policy text as the control. Transparent return and refund language helps, but it does not replace evidence quality, support responsiveness, and case-level decisioning. Merchants should also be careful not to overfit to chargeback outcomes alone, because some valid customer complaints never become formal disputes. A balanced program protects revenue, preserves goodwill, and creates an auditable path for disputed claims without turning every good-faith request into a fraud exam.

Related resources from NHI Mgmt Group

• How can merchants reduce fraud without blocking good customers?
• How should security teams reduce account recovery risk without making sign-in harder?
• How should ecommerce teams handle AI-generated return claims without overblocking good customers?
• How should PBMs reduce account takeover risk without making member access harder?