Return fraud prevention and what to look for - Ekata, a Mastercard company

Return fraud: What to look for and how to prevent it

As we have detailed in previous posts, as worldwide ecommerce sales continue to grow, ecommerce fraud increasing; with Juniper Research predicting global losses to exceed $48 billion by the end of 2023. One such type of fraud is return fraud and abuse. It’s so prolific that the National Retail Federation (NRF) estimates more than $100 billion in merchandise was returned fraudulently in the past 12 months in the United States alone.


Article at a glance:

What is return fraud and what are the different types of return fraud?
What are the consequences of return fraud for retailers?
What can retailers do to detect and prevent return fraud, and what innovative tools are available to help them?


Let’s now break down what return fraud is, the different types of return fraud, detail some real-life cases of return fraud and explore into how merchants can prevent it in 2024 and beyond.
What is return fraud and how to stop losing revenue

What is return fraud?

Return fraud is a type of payment fraud that abuses a company’s return policy. Return fraud involves a person returning an item to a retailer despite that item not qualifying for a return or refund, whether intentionally or on accident.  For example, the item may be stolen, counterfeit, already used or even purchased from a different retailer altogether.

There are occasions where return fraud can be considered a type of friendly fraud, particularly when the product is purchased in earnest (or perhaps in error) and then the charge is disputed later, resulting in a chargeback.

Whether an honest mistake (“friendly fraud”) or intentional abuse, return fraud can present itself in a multitude of ways. Some common forms of return fraud include:

  • Wardrobing

    Wardrobing is when a customer purchases an item such as clothing or an accessory, wears it and returns it for a full refund. It is so prevalent that, in a recent retail industry report, the National Retail Federation reported, that almost half of respondents said they had returns of used, non-defective merchandise.
    Example: Real-life example: One of the more shocking real-life examples of wardrobing involves Mrs. America herself. In 2016, Jennifer Susan Kline not only switched labels on designer clothes and returned them (more on that below) but returned worn items to a popular national department store. All up her swindle cost the department store more than $5,000.

  • Renting

    Similarly, “renting” an item means exactly how it sounds: a customer purchases an item, perhaps for a job interview or a wedding, only to then return it under the retailer’s return policy after they’ve already worn it.

  • Switching

    This type of return fraud is particularly unscrupulous. A customer replaces a purchased item with another of lesser quality (perhaps cheaper) and then returns it, claiming it is the one they originally bought.
    Example: How about this extraordinary case? As reported in Newsweek, in 2019, a fraudster was found to have scammed Amazon out of nearly $370,000 by returning packages filled with dirt. The returned boxes, equal in weight to the original item purchased, would trigger a refund from Amazon and the fraudster would go on to sell the original item. This return fraud scenario is said to have been the biggest Amazon scam ever recorded in Europe. 

  • Bricking

    This involves a fraudster returning an electronic item upon stripping it of its valuable parts and reselling those parts for a profit.

  • Price arbitrage

    This type of return fraud often involves two retailers. A customer purchases the same item at two locations and returns the cheaper one to the store with the higher cost to pocket the price difference.
    Example: One such example of this form of return abuse involves a Florida woman defrauding a US department store out of thousands of dollars by claiming counterfeit designer handbags were original handbags and pocketing the huge refunds. While it worked the first few times, investigators became suspicious of the number of bags the woman started returning.

  • Stolen merchandise

    This involves a bad actor using a stolen credit card to purchase an item, only to return the item for cash in-store.

  • Double-dipping

    This occurs when a fraudster claims they never received an item they ordered. Not only do they go to the retailer to seek a refund, but they also put in a request for a chargeback. Worse still? They receive the item and keep it!

ecommerce returns and preventing return fraud

What are the consequences of return fraud?

The abuse of return policies can have serious consequences for retailers. In fact, most recent statistics from the NRF indicate that in the United States alone, return abuse and fraud cost the sector $101 billion in 2023. This cost includes the overall impact of processing returns, restocking items and absorbing the loss of fraudulently returned merchandise.

Furthermore, these financial consequences ultimately impact prices. Indeed, to compensate for the increase in operating costs related to return abuse, retailers must adjust prices for all customers.

Naturally, should a retailer adjust their returns policy in response to ongoing return abuse, making it tougher for legitimate returns to take place, customer trust is eroded. In such a competitive market, retailers cannot afford to lose customers.

What can retailers do to detect and prevent return fraud?

Retailers are in an unenviable position when it comes to return fraud. Should they make their returns policy too strict, they risk losing customers to the competition. However, a returns policy that’s too relaxed leaves the door open to bad actors.

Fortunately, there are innovative tools in place that leverage the power of machine learning and behavioral analytics to help retailers verify customers and identify risky behavior.

Mastercard enables retailers a comprehensive view of their customer’s digital identity, integrated into their decisioning platform, rules engine and fraud risk models. Using sophisticated machine learning technologies and data science, the Identity Engine combines two exclusive datasets, the Identity Graph and the Identity Network.

Specifically, the Identity Graph examines the relationship and the validity of a customer’s identity elements, such as name, phone, email, address and IP address and acts as an ideal authoritative source to identify fraudulent identities. Investigating how identity elements are linked together, the Identity Graph asks:

  • Does this email belong to this customer?
  • Is this address residential?
  • Is this phone number valid? What type of number is it?

Meanwhile, the Identity Network looks for behavioral patterns and anomalies, vital when it comes to repeat offenders of refund abuse. Aggregated from real-world digital interactions, the Identity Network can predict fraudulent versus legitimate behavioral patterns by analyzing how a customer’s identity elements are being used online. Some of the questions the Network might ask include:

  • When was this email first seen in the digital interaction? Is this IP address risky?
  • Has there been an increase in the number of interactions from this email/phone number/IP address in the past hour?

One such reason for this seasonal surge is an increase in foot traffic (both in store and online) and fraudsters taking advantage of new hires. As Alan Amling, a supply-chain professor at the University of Tennessee explains in the Wall Street Journal,

“[During the holidays] stores typically have people that are new or temporary seasonal hires, and they’re easier marks for criminals.”

Therefore, customer service training, along with consistent education to use and understand fraud detection technologies and, in turn, leverage data and analytics to identify potential instances of return abuse is important.

To learn more about how you can leverage the proper return fraud solution to flag suspicious behavior without impacting the user experience of genuine customers, get in touch today.

 

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