How DTC Brands Can Reduce Refund Fraud During Returns

TL;DR Summary

DTC brands face higher refund fraud exposure than omnichannel retailers because they operate without the in-person inspection and friction-inducing delays that a physical store provides. Per the NRF and Happy Returns 2025 Retail Returns Landscape report, about 9% of the $849.9 billion in U.S. returns, roughly $76 billion, involves fraud.

To reduce refund fraud, ops and finance teams should apply seven controls in sequence: category-specific return windows, photo or video proof at RMA, ML fraud scoring with velocity thresholds, exchange-first refund deflection, single-use RMA token locking, payment gateway controls, and a chargeback representment workflow. Layering LTV-tiered policy segmentation on top protects your best customers from over-blocking.

Introduction

A traditional retailer has a store associate, a printed receipt, and an ID check standing between a fraudster and a refund. A pure-play DTC brand has none of those. Without those in-person verification points, DTC brands need stronger controls across returns, refunds, and payments.

The scale of the problem is significant. The NRF and Happy Returns 2025 Retail Returns Landscape report found that 9% of all U.S. returns are fraudulent, amounting to roughly $76 billion of the $849.9 billion returned in 2025.

Two different problems are present in that figure. Return fraud involves sending back a wrong, used, counterfeit, or empty package to claim unearned credit. Refund fraud exploits the payment or claims process to obtain money without a valid return. Warehouse and operations teams typically handle the first, while finance and customer service play a larger role in detecting and resolving the second.

This guide explains how DTC brands can address both types of fraud through seven practical controls, from return-window and RMA rules to payment safeguards and chargeback representment. It also covers how to assign ownership across teams and adjust fraud controls based on customer value.

How to reduce refund fraud in e-commerce: At a Glance

Why D2C Brands Are More Exposed to Refund Fraud Than Omnichannel Retailers

Pure-play DTC brands have a wider fraud surface than omnichannel retailers because the verification touchpoints a store provides are missing online. A store return involves a person, a receipt, and a physical inspection. A DTC return is often a form submission followed by an automatic credit.

The blind spot widens for brands that outsource fulfillment. Third-party logistics staff inspect returns without any financial stake in catching fraud, so warehouse-level detection gaps are common.

On top of that, digital-native fraud vectors scale in ways that in-store fraud never did. Sumsub's Q1 2025 identity fraud data showed that synthetic identity document fraud in North America surged more than 300% year-over-year, driven by generative AI that manufactures fake IDs at scale.

The pressure carried into the following year. The 2026 LexisNexis Cybercrime Report, covering full-year 2025 activity across 116 billion transactions, found the e-commerce fraud attack rate up 64% year over year and login-stage attacks, where fraudsters try to take over or create accounts, up 216%. These are account-focused attacks that omnichannel retailers rarely face at the same volume.

Return Fraud vs. Refund Fraud: A Taxonomy DTC Ops Teams Can Actually Use

Return fraud involves sending back a wrong, used, or counterfeit item to claim credit you did not earn. Refund fraud involves exploiting the payment or claims process to obtain a refund without a valid return. The operational difference that matters for DTC brands is ownership: your warehouse team detects return fraud, while your finance and CS teams detect refund fraud. Get that ownership split wrong, and both slip through.

The eight fraud types every DTC ops team should be able to name:

Return fraud targets your inventory integrity; refund fraud targets your cash flow. In DTC operations, they need different detection systems, different team ownership, and different recovery workflows, which is exactly why a single blanket returns policy leaves gaps in both.

The Real Cost of Refund Fraud for DTC Brands: 2026 Benchmarks

Per the NRF 2025 report, approximately 9% of U.S. returns, roughly $76 billion of the $849.9 billion total, involve fraud. That figure sits below some headline numbers from prior years, and the gap is mostly due to a change in methodology rather than a real decline, as the note under the table explains. The finance-relevant benchmarks matter before you set a fraud tolerance.

The $76 billion and $103 billion estimates above are not a like-for-like comparison. They should not be read as evidence that return fraud fell from one year to the next. They come from different datasets and methodologies: the $103 billion estimate uses 2024 Appriss Retail and Deloitte data, while the $76 billion figure comes from the NRF and Happy Returns 2025 research. They are better treated as separate estimates of the scale of return fraud.

Finance teams should also watch the payment side. Under the Visa Acquirer Monitoring Program (VAMP), with updated thresholds effective June 1, 2025, Visa folded its former dispute and fraud monitoring programs into a single count-based ratio.

A merchant whose fraud and dispute activity pushes past program thresholds gets flagged for mandatory risk controls, so tracking your dispute ratio is now a board-level metric, not a back-office one.

7 Strategies to Reduce Refund Fraud in Your DTC Returns Process

1. Implement Category-Specific Return Windows

Set return windows by product category instead of a single sitewide rule: 14 days for electronics, 30 for apparel, 60 for home goods. A blanket 60-day window on electronics gives a wardrobing buyer maximum time to use and return the item.

Segment the windows in your returns platform and surface the category-specific deadline in both the returns portal and the confirmation email. This narrows the window for wardrobing and late returns in high-risk categories while leaving low-risk categories untouched, so legitimate buyers rarely notice the change.

2. Require Photo or Video Documentation Before RMA Approval

Gate return merchandise authorization on uploaded photo or video evidence of the item's condition or defect. Collecting photos and serial numbers at claim submission builds an evidence trail that deters false damage claims upfront.

Make the upload a hard gate in your pre-authorization flow, not an optional step. The record you capture here does double duty: it stops the fake claim now and becomes the evidence you need to win a chargeback dispute later.

3. Use ML-Based Fraud Scoring with Velocity Thresholds

Apply machine learning risk scores to return requests using behavioral signals: return frequency, order-to-return timing, geographic clustering, device fingerprint, and account age. Set a velocity threshold, such as flagging any account with three or more returns in 30 days.

Rule-based blocklists only catch known fraudsters; ML scoring catches pattern-based fraud from brand-new accounts, including the synthetic identity accounts covered earlier.

Integrate a scoring layer via a provider such as Signifyd, Kount, or Riskified, or use native returns-platform features. Of these signals, the velocity threshold is the one a lean ops team can usually ship first, since it needs no data science resources to configure.

4. Shift Refunds Toward Exchanges and Store Credit

Default the returns flow to exchange or store credit rather than a cash refund, and add a small 5 to 10% bonus credit to lift voluntary adoption. Most refund fraud depends on extracting cash, so routing the default toward credit removes the payout that makes those schemes worthwhile.

Claimlane's returns KPI benchmarks note that presenting exchange and credit options before the refund option, and making exchanges frictionless, is what actually moves the exchange-to-refund ratio. Configure your portal so that a cash refund takes one extra click or a short delay.

5. Implement RMA Token Locking to Prevent Duplicate Claims

Assign each approved return a unique RMA token that expires upon use and is locked to the original order, shipping address, and account. Duplicate RMA exploits, where the same return is submitted repeatedly or across multiple accounts, are a recurring vector.

Your OMS or returns platform should generate one-time codes tied to order-level data and include cross-account deduplication checks. Compared with the ML scoring in Strategy 3, this is a cheaper control to stand up because it relies on deterministic rules rather than a model, and it also cleans up your reconciliation audit trail.

6. Apply Payment Gateway Controls for Refund-Side Risk

Turn on 3D Secure and Address Verification Service at your payment processor to reduce the refund-to-stolen-card vector. Then audit your refund-versus-void decision flow: voiding a charge before settlement costs nothing, while refunding after settlement costs interchange fees and processing charges.

Many DTC brands default to refunds even when a free void was available. Map your order-to-settlement timing and set automatic void triggers for same-day cancellations. Keep the VAMP thresholds in view here, since every avoidable dispute counts against your ratio.

7. Build a Chargeback Representment Workflow

Create a documented process for disputing fraudulent chargebacks. Compile the order confirmation, delivery confirmation, RMA photo evidence, and IP or device logs into a standard evidence package, then submit through your processor's representment portal inside the dispute window, typically 20 to 45 days.

A payment reversals report suggests that merchants win roughly 45% of disputes when they submit evidence, with the win rate driven by reason code and evidence quality. Prevention controls stop fraud before a refund is issued; representment is what recovers revenue after a fraudulent chargeback has already gone through. Assign dispute ownership explicitly in your RACI so the evidence package is compiled at the time of return authorization, not scrambled together as the dispute window closes.

Adjust Fraud Controls by Customer Value and Risk

LTV-Tiered Controls

Not every customer should face the same fraud gate. High-LTV customers, your top 20% by purchase history, should get a friction-reduced flow: auto-approved RMAs, no photo requirement, and extended windows. First-time and low-LTV buyers should meet the full gate stack.

Over-blocking is a loss in itself. A customer with 24 lifetime orders and $3,000 in spend, wrongly flagged as a fraudster, is a retention failure disguised as a fraud win. The prerequisite is feeding real-time LTV signals into the returns authorization decision, which is a customer segmentation data problem before it is a fraud problem.

Cross-Functional RACI

Fraud prevention fails when nobody owns the specific step. This four-role map fixes that:

 R = Responsible, A = Accountable, C = Consulted, I = Informed.

Where ClickPost Fits in the Framework

The controls above are platform-agnostic; you can build most of them on several returns and post-purchase stacks. For teams already evaluating ClickPost, here is which product maps to which control, so you can judge the fit against what you would otherwise assemble from separate tools.

• Customer Segmentation carries the LTV signals that the tiered-control approach depends on (as shown in the LTV section above).

• Returns is where category-specific windows and persona-based RMA rules get configured (Strategies 1 and 2).

• Exchanges runs the exchange-first flow that shifts refunds toward credit (Strategy 4).

• Tracking and Post-Purchase hold the delivery confirmation data a representment package needs as evidence (Strategy 7).

• Order Editing lets customers correct an address before shipment, which removes one common pretext for item-not-received claims.

• Returns Protection absorbs the financial exposure from returns that slip past controls for teams that want that backstop.

Refund Fraud Prevention Checklist for DTC Ops Teams

• Set category-specific return windows by product vertical.

• Require photo or video proof as a hard gate before RMA approval.

• Score every return with ML and a 3-returns-in-30-days velocity flag.

• Default the flow to exchange or store credit with a bonus incentive.

• Lock each RMA to a single-use, order-bound token.

• Enable 3DS and AVS, and void before settlement where you can.

• Keep a pre-built chargeback evidence package ready for representment.

Conclusion

DTC structural exposure to refund fraud is real, but it is addressable. Applied in sequence and owned across ops and finance, the seven-strategy framework moves a brand from reactive fraud cleanup to proactive prevention.

The next frontier is already here: AI-generated damage photos and synthetic identities will require platform-level detection that no single rule can catch. Reducing refund fraud in DTC is not primarily a technology problem. It is a cross-functional ownership problem that technology makes solvable.

Frequently Asked Questions

What is the difference between return fraud and refund fraud?

Return fraud involves sending back a wrong, used, or counterfeit item to claim credit. Refund fraud exploits the payment or claims process to get a refund without a valid return. Ops owns return fraud detection; finance owns refund fraud.

What percentage of e-commerce returns are fraudulent in 2025?

Per NRF 2025, about 9% of U.S. returns, roughly $76 billion of $849.9 billion total, involve fraud. This uses a revised NRF survey methodology and is not directly comparable to earlier Appriss estimates near $103 billion.

How do DTC brands detect fraudulent return requests?

Six signals: three or more returns within 30 days, geographic clustering of claims, device fingerprint mismatch, RMA reconciliation gaps in the OMS, account age under 30 days, and refund address not matching the order address.

What is FTID fraud and how does it work?

Fake Tracking ID fraud manipulates a real tracking number to show that an empty or wrong package was delivered, then claims a refund. Detecting it requires reconciling carrier-level weight and scan data against the shipment.

How can DTC brands prevent friendly fraud chargebacks?

Require photo documentation at the RMA stage, confirm delivery using carrier scan data, and maintain a pre-built evidence package containing the order confirmation, IP log, and delivery proof. Submit representation inside the processor's dispute window.

Should DTC brands offer store credit instead of cash refunds?

Yes, for fraud reduction. Defaulting to store credit removes the cash extraction incentive behind most refund fraud. A 5 to 10% bonus credit lifts voluntary adoption without restricting a customer's right of return.

What tools do ecommerce brands use to prevent return abuse?

Three categories: ML fraud scoring (Signifyd, Kount, Riskified), returns platforms with RMA token systems (Loop, ClickPost), and payment gateway controls (Stripe Radar, Braintree). Match the tool tier to your GMV.

How do you build a return fraud scoring system?

Start with three velocity rules for frequency, timing, and geography, then layer device fingerprint and account age. Score each request 1 to 10 at authorization: auto-deny at 8 and up, manual review at 5 to 7, auto-approve below 5.