Return fraud statistics: 12 months of real Shopify refund data
Last reviewed ·By Adrien Bokor, founder of RefundSentry
Most return fraud statistics recycle the same industry surveys. This page is different: we analyzed 73,174 orders, 2,116 refunds and 861,588 EUR of refunded value from a single Shopify store (a French DTC cosmetics brand, anonymized with permission) over 12 months, June 2025 to June 2026, including all 69 of its bank disputes. Every number was re-queried from production data before publication. The null results are published alongside the findings.
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The findings
Nine findings, each with the number, the context, and what it means for a merchant reading their own refund line. Unless stated otherwise, percentages use shipped-order refunds as the denominator (see methodology).
46% of refunded value was on orders that never shipped
Almost half the refund line never involved a shipped parcel
Of 861,588 EUR refunded over the year, 398,552 EUR (834 refunds) sat on orders that were never fulfilled. 701 of those 821 orders were formally cancelled before shipment. That money is pre-shipment cancellations: payment issues, address problems, buyer remorse caught early. It is not returns abuse.
The abuse surface is the other half: 463,036 EUR across 1,282 refunds on orders that actually shipped. Every abuse statistic below uses that shipped slice as its denominator. A refund dashboard that mixes the two halves into one number overstates the abuse problem by roughly 2x, and hides where the real loss patterns live.
69 / 69 payment disputes came from orders Shopify's fraud analysis rated LOW risk
Shopify's fraud analysis rated every order that became a chargeback as low risk
Shopify's native fraud analysis covered all 73,174 orders in the dataset. Over 12 months it flagged 4 orders HIGH risk and 47 MEDIUM. The 69 orders that went on to become actual payment disputes, worth 35,442 EUR, were all rated LOW. Every single one.
That is not a bug in Shopify. Checkout fraud tools answer one question: is this a stolen card? They are good at it. Post-purchase abuse is invisible to them by design, because the card is real, the billing address matches, and the IP is the customer's home connection. The fraud starts after the parcel ships, which is the part no checkout-time score can see.
74.8% of shipped-refund value went to one-and-done customers
Three quarters of refunded value went to customers who never came back
913 customers placed a first order, got it refunded, and were never seen again. Together they took 346,315 EUR, three quarters of all post-shipment refund value. For each of them the store paid the acquisition cost, the product, the outbound shipping, and then the refund, and got zero lifetime value back.
To be clear about what this is: most of these customers are unhappy buyers, not fraudsters. The finding is about economics, not blame. Refund losses concentrate exactly where customer-acquisition spend was just burned, which makes the refund line a marketing-efficiency problem as much as a fraud problem.
1 in 6 customers who filed a chargeback came back and ordered again
Customers who filed a chargeback kept ordering, and the store kept shipping
68 customers filed a chargeback during the year. 11 of them returned and placed 14 more orders. The store shipped every one, because nothing in the default stack flags a past-chargeback customer at order time.
It went further: 17 chargeback customers received new refunds after their dispute, 18 refunds worth 8,736 EUR. One concrete case: a 629 EUR chargeback in late December, followed by two more accepted orders through mid-February. Another: a 520 EUR chargeback in early February, another order shipped in May.
64% of bank disputes claimed the package never arrived
“It never arrived” was the dominant dispute reason, and most claims were checkable
44 of the 69 bank disputes (64%) were item-not-received claims, worth 20,375 EUR. 37 of those 44 (84%) had a carrier tracking number that could have been checked against delivery confirmation. Nobody checked.
In France, where this store ships, La Poste exposes delivery confirmation for Colissimo and Chronopost parcels at no cost. A tracking lookup at dispute time would have produced evidence for the large majority of these claims, either confirming a real delivery failure or contradicting the claim.
23 customers with two or more shipped orders had 100% of those orders refunded: 17,535 EUR of product, shipped, refunded, and mostly never returned. Per order, each transaction looks like a normal purchase followed by a normal refund.
This is the cleanest example of a pattern that is invisible in aggregates. No per-order view surfaces it. It only appears when refund history is rolled up to the customer level, which is exactly the rollup most refund dashboards do not do.
2.3% of refund value came from the top 10 refunders
Refund abuse was diffuse, not concentrated. That is why dashboards miss it
The popular story says a handful of bad actors drive most return loss. On this store the data says otherwise: the top 10 refunders accounted for 2.3% of refund value, and customers with 3 or more refunds for just 1.6%. The loss was spread across 1,971 refunding customers, most of them one-timers.
Repeat behavior still carries signal. The 113 customers who refunded twice or more averaged 690 EUR of lifetime refunds against 422 EUR for one-time refunders, 63% higher. And when a customer's second refund came, it came fast: the median gap between a repeat refunder's first two refunds was about 2 days. Repeat refunding arrives in bursts, not as a slow habit.
Rolled up to the customer level, refunds belonging to customers our analysis flagged MEDIUM risk or higher came to 171,633 EUR, about 1 in 5 refund euros.
2.7x higher refund probability on 700+ EUR orders vs sub-100 EUR orders
Refund probability climbs with basket size
Orders under 100 EUR refunded at 1.5%. Orders over 700 EUR refunded at 4.0%, a 2.7x gradient. Bigger baskets carry disproportionate refund exposure, which matters when deciding which orders deserve a manual look before fulfillment.
+34% relative refund-rate lift on influencer-code orders
Discounted orders refunded more often
183 refunds (8.7% of all refunds) sat on abnormally discounted orders, worth 69,365 EUR. Orders placed with influencer discount codes refunded at 3.2% to 3.8% against a 2.85% store baseline, up to 34% higher in relative terms.
20% of refunds (431) were for the full order value, totalling 221,948 EUR.
of shipped-refund value went out in December and January. Returns season is real, and it is concentrated.
3x less
Multi-shade orders refunded at 0.83% against the 2.85% baseline. In cosmetics, the multi-variant buyer is an enthusiast, not a bracketer. The opposite of what apparel data would predict.
0.6%
Bracketing proper (ordering multiple variants to keep one) produced 13 refunds worth 7,198 EUR. A defining pattern in apparel, marginal in cosmetics. Fraud patterns are vertical-specific.
3.7x
The worst product refunded at 5.9% of its orders against 1.6% for the best, a 3.7x spread. Product-level refund rates varied far more than customer geography or email domain ever did.
25
refunds were issued 90 to 182 days after the order, worth 12,859 EUR. Only 2 were forced by a chargeback. The rest were goodwill outside any plausible return window.
What we checked and did not find
Negative results rarely get published, which is how the same myths keep circulating. These angles were tested on the same dataset and came back flat:
Shared-address clusters barely refunded. The biggest multi-account addresses turned out to be families and pickup points, not rings.
Geography was flat. No region refunded meaningfully above baseline.
Email domain told us nothing: mainstream providers refunded at 2.91%, custom domains at 2.86%.
Guest checkout was a non-factor: 1 refund out of 2,116 came from a guest order.
Refunds did not cluster against the return-window deadline. 56% of refunds landed within 7 days of the order.
One caveat on scope: this is a single store in a single vertical. The bracketing and multi-variant numbers in particular would look different on an apparel store, and the null results above are store-level observations, not category laws.
Methodology
Source.Production data from one Shopify store, a French direct-to-consumer cosmetics brand, analyzed with the merchant's permission and anonymized. Window: June 2025 to June 2026. Base: 73,174 orders, 2,116 refunds, 861,588 EUR refunded, 1,971 refunding customers, 69 bank disputes.
Shipped vs cancelled. Refunds were split by whether the underlying order was ever fulfilled. 834 refunds (398,552 EUR) sat on never-shipped orders; 1,282 refunds (463,036 EUR) on shipped orders. Abuse statistics use the shipped slice as denominator, because a refund on a cancelled order cannot be returns abuse.
Wording.We say a customer "filed a chargeback", not "is a fraudster". Risk flags describe statistical patterns, not verdicts about people. Most one-and-done refund customers are unhappy buyers, and the analysis is explicit about that.
Limitations.Single store, single vertical, single country. Lifetime value after a refund was not measured. Refund-method splits were excluded because a third of refund value sat in an unexplained "other" bucket we have not yet attributed. Numbers are exact as of the June 2026 re-query and supersede an earlier internal revision of this analysis.
From 12 months of production data (June 2025 to June 2026) for a single French direct-to-consumer cosmetics brand on Shopify: 73,174 orders, 2,116 refunds, 861,588 EUR of refunded value, 1,971 refunding customers and 69 bank disputes. The analysis was run by RefundSentry with the merchant's permission, and every number was re-queried from the live database before publication.
What share of refunds is actually fraud or abuse?
Less than the refund line suggests. 46% of refunded value was on orders that never shipped (mostly formal cancellations), which is not abuse at all. On the shipped half, the loss was diffuse: the top 10 refunders held 2.3% of refund value. Rolled up to customer level, about 1 in 5 refund euros traced to customers flagged as elevated risk. The honest summary is that most refund loss is a policy and economics problem, with a real abusive slice inside it.
Why didn't Shopify's built-in fraud analysis catch these patterns?
Because it isn't built to. Shopify's fraud analysis estimates the risk that a payment uses a stolen card, and it does that job well. In this dataset it rated all 69 orders that later became payment disputes as low risk, because the cards were real and the addresses matched. Post-purchase abuse (refund abuse, item-not-received claims, repeat chargebacks) happens after checkout, where checkout-time scoring cannot see it.
Can I cite these statistics?
Yes. Cite them as “RefundSentry return fraud research, 2026” with a link to this page. The merchant is anonymized (a French DTC cosmetics brand) and individual customers are not identifiable. If you need the methodology behind a specific number, the definitions are on this page; for anything else, contact us.
Last reviewed: 12 June 2026. Disclosure: RefundSentry sells fraud-intelligence software for Shopify, so we have skin in the game on the detection framing. The numbers above stand on their own.