Refund abuse KPIs: How to quantify and reduce refund fraud rates

Whether perpetrated by professional criminals or opportunist first parties, returns and refund abuse is a pain point for the majority of ecommerce companies.

The latest Ravelin data gathered by surveying ecommerce enterprises around the world demonstrates just how prominent the issue has become:

• Over half (55%) of merchants struggle to quantify their refund abuse problem.

• 54% report a rise in refund abuse in 2026 compared to the previous year.

• Almost 7 in 10 (67%) received fake AI-generated or AI-doctored refund evidence.

It’s certainly a good time to consider strategies and best practices around assessing and quantifying refund abuse as a merchant – as well as to measure whether your refund abuse strategy is working.

Ditching the anecdotal for a data-focused approach

Refund abusers are gaming generous online shopping policies – with 1 in 4 shoppers admitting to abusing refund policies in the past year. Yet, from the above findings and our discussions within ecommerce, it would be fair to say that, much of the time, merchants know that they have a refund abuse problem but don't know how to quantify it.

Of course, this directly affects the resources dedicated to resolving this problem.

If a fraud team cannot demonstrate or confidently measure the issue, achieving buy-in is not easy for fraud managers. When your fraud landscape requires a revisit of your refund policies, it may be difficult to demonstrate exactly why and what would work without clear, granular metrics.

Similarly, a customer service team may be able to tell there is something wrong with certain requests but lack the techniques, training, or cross-team enablement to address the red flags. In fact, 55% of ecommerce enterprises said that their customer service team “lack the tools and training to effectively challenge suspicious refund claims” per the Global Fraud Trends 2026 report.

The following key refund abuse KPIs will help you to start quantifying refund abuse and its toll on your company, no mmatter whether you’re an online retailer struggling with wardrobing, a travel agent struggling with fraud, a digital goods company, or an online marketplace with a complex threatscape.

Why is refund abuse so difficult to measure?

Traditionally, online fraud teams focused their efforts on payment fraud, where chargeback rates and block rates are objective, fairly easy-to-measure metrics. Dispute information arrives from the bank, and merchants will use it to understand what might have gone wrong.

The fraud of 2026 is not as simple. Although they both can result in the shopper receiving their money back, refunds and chargebacks are very different.

Crucially, even after a refund claim has been accepted or rejected, there is no obvious, undisputed signal on whether it was fraudulent or legitimate.

False negatives are therefore easy to miss, as are false positives. As a result, uncertainty looms large when it comes to dealing with refund abuse as opposed to chargeback fraud.

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KPIs to measure your refund abuse problem

To understand whether you have a refund abuse problem and measure your efforts to reduce refund abuse, use KPIs such as refund rate, total amount refunded, first order refund rate and reorder rate following a refund.

Let’s take a look at each of these refund abuse metrics in detail:

1. Refund Rate

This is your headline rate and the first thing to consider. Is the rate of refunds stable or rising?

For the purposes of measuring refund abuse, a suitable Refund Rate to look at is the weekly refund value as a share of total spend.

This is attributed to the original order date rather than when the refund was requested or issued, because the most recent weeks may be understated as refunds are still accumulating.

As a merchant, looking at this overall refund rate will give you a top-down, zoomed-out view of whether there is any suspicious activity at the movement – especially once you establish what the baseline rate is for your customer base, and you see diversion.

2. Total Amount Refunded

This metric gives you a quick indication of the financial scale for your organization.

Measured in the merchant’s local currency, Total Amount Refunded is the total value of approved refunds in the past 90 days (rolling), broken down by week.

Sudden spikes in this figure can indicate a new refund abuse-focused fraud-as-a-service scheme targeting you, or a new loophole that refund abusers have found to take advantage of you.

3. First-Order Refund Rate

This metric homes in on a specific risky segment.

The First-Order Refund Rate is the percentage of new accounts/new customers who requested a refund for their very first order within the policy window.

This is an important refund abuse KPI to keep track of because of the practice of burner accounts, where both professional and amateur fraudsters set up new customer accounts to conduct their schemes with a “clean slate” – hoping that fraud defenses do not pick up on their previous activity on other accounts.

Granted, first-order refund rate will not always reflect fraudulent activity. Perhaps your refund policies ought to be stricter? Or maybe certain types of merchandise incur more refunds, hinting at a low-quality supplier or manufacturing defects?

If a lot of new customers request refunds, this is something a merchant will always find interesting to look into – fraud or not.

4. Reorder Value Following a Refund

Then, it’s time to look at the recovery outcome.

The Reorder Value Following a Refund is the number of customers who placed a new order within the median time window divided by the total number of refunded customers.

The idea behind this is that genuine customers are likely to want to shop with you again, especially if the item they previously returned was not suitable.

What’s more, even if it’s not related to fraud, this is something merchants are striving for: repeat custom, loyalty and low return rates overall.

You’ll want to consider this metric in relation to the specific industry you operate in and your understanding of your legitimate customers’ habits. Here are some examples:

• Size-related reorder rate for garments: In the retail clothing sector, one would reasonably expect returns for ill-fitting garments to often be followed by a new order for the same garment in a different size.
• Star ratings in food delivery: In food delivery, returns are not possible but refunds may occur more frequently for restaurants and shops with lower star ratings. Even though a customer may not prefer the same eatery, this would not reasonably affect their overall ordering rate. In other words, platform loyalty would not be affected, even if a customer is dissatisfied with a specific restaurant – or even a specific rider.
• Rebooking after a refunded trip: In the travel ticketing sector, it’s a good idea to keep track of the number of customers who book a new trip following a refund, divided by all refunded customers. A traveler who was refunded because a trip genuinely fell through usually still wants to travel, and will often rebook the same route or destination. On the other hand, an account that takes refunds repeatedly but never rebooks looks less like a frustrated customer and more like someone extracting value.

Seasonal spikes in refunds

There are certain times of the year when refund requests are more likely to come in.

For example, the lead up to Christmas starting with Black Friday and Cyber Week typically involves increased sales figures, and refunds for these (including legitimate requests) are likely to come in January.

There are also periods unique to individual companies that are more likely to attract attention from fraudsters, including opportunist customers. A marketing push can invite fraud and abuse, but also typically attracts more sales.

It’s important for any merchant to keep these types of fluctuations in mind when interpreting refund abuse KPIs and reviewing their strategy.

Takeaways and opportunities around quantifying refund abuse

Unlike traditional ecommerce payment fraud, refund abuse and return fraud has particularities that require you to keep an eye on a range of metrics.

Refunds make ecommerce fraud management more complex, but they also offer opportunities.

For instance, money that is refunded is more likely to be spent. In addition to preventing abusive behavior, a great refund abuse solution will allow you to serve those good customers better, building a loyal customer base who will enjoy shopping with you and recommend you to their friends.

You can rely on Ravelin to support you with identifying the metrics that are important to your business, building detailed reporting that showcases the issue, and deploying a strategy that works for you.

The ultimate goal? To understand refund abuse more deeply, prevent it better, and power your company's secure growth.

Related content

• On-demand webinar: Refund abuse – quantifying, preventing and getting buy-in
• AI-powered refund abuse and dispute fraud: The democratization of deception
• 5 types of refund abuser you’ll meet after the peak period sales boom – and how to deal with them
• Experts share 5 return & refund abuse prevention strategies (from the stage of RavCon)
• Dev docs: Refund abuse integration
  
• The State of Refund Abuse – 2026 report