If you’re an online retailer, you’ll be well familiar with the perennially unsatisfied consumer who demands services, discounts, and refunds at every turn. As long as people have sold things, there have been tough customers. But, in online sales, these demanding buyers aren’t always customers — often, they’re fraudsters, who claim that all of their online purchases had problems that required a refund.
Taking returns in isolation, it can be very difficult to differentiate the tough customers from the fraudsters. That makes this type of first-party or “friendly fraud” a serious, hidden cost for many online businesses.
Based on a recent, in-depth survey of friendly fraud conducted by Fraud.net, we saw bad news, and good news. The bad news is that it’s an extremely pervasive, and costly form of fraud for online vendors and banks. The good news is that, with the proper procedures in place, friendly fraud is detectable and preventable.
What is friendly fraud?
Friendly fraud occurs when consumers purchase goods or services, then get their money back by claiming they never made the purchase, didn’t receive the product, or only received part of their order. Most merchants consider it a cost of doing business because it’s so difficult to track.
Businesses are often reluctant to identify friendly fraud out of a desire to provide outstanding customer service and frictionless returns. Most of the time, they issue refunds without investigating the matter further because keeping customers satisfied gives them a competitive edge. Financial institutions can inadvertently exacerbate this problem, siding with consumers in transaction disputes by default.
The type of fraud is so slippery that if you’re an online business, you may not be tracking it at all, lumping it in with legitimate returns. That is a costly error.
The High Price of Hidden Fraud
We estimate that friendly fraud could reduce your legitimate sales by 1%, and reduce your profit margin by as much as 20%.
Those numbers sound impossibly large but keep in mind that, besides issuing refunds, you could incur the cost of the goods and services provided, chargeback fees, order fulfillment costs, and the original customer acquisition cost. In the case of physical goods, you could also lose a potential legitimate sale and new customer acquisition since the goods are no longer available.
So, what is the true extent of friendly fraud? Fraud.net conducted a survey — perhaps the most extensive ever conducted — to answer this question.
We randomly selected 100,000 transactions with a negative outcome that took place over three years. After accounting for merchant error, honest mistakes by customers, and third-party fraud like identity theft, 44% of the remaining transactions met our criteria for friendly fraud.
Traditional fraud prevention doesn’t work
In friendly fraud, scammers don’t hide their identity. Common techniques like ID verification that are effective against third-party fraud just don’t work.
That can be seen in risk scoring of friendly fraud transactions, where known friendly fraud purchases show just 16 percent of the risk of a traditional third-party fraud.
Even once the fraud has been identified, businesses seem reticent to blacklist perpetrators. On average, a friendly fraudster will get away with nine fraudulent claims before they’re shut down, versus about three for traditional third-party fraud.
It’s possible that businesses give these customers the benefit of the doubt or hope to recoup some of the money they lost in future sales from them. Whatever the rationale, this attitude creates an environment where fraudsters can take advantage of return policies and keep targeting the same businesses, over and over, with no consequences.
A comprehensive solution
Friendly fraud could be costing the industry as much as $50 billion each year according to Mercator Advisory Group. It’s time to stop considering it a cost of doing business and approach it as a problem that needs a comprehensive solution.
The ideas listed below are good places to start:
Consortium data. Merchants and payment processors can collaborate and share data. Once a merchant identifies a friendly fraudster, their digital ID can be shared with other members of the consortium. Red flags will go up if a fraudster seeks refunds on multiple purchases across vendors while not actually paying for anything.
First-party monitoring. A first-party monitoring system associates a unique identifier with each customer. This system tracks their shopping behavior across time and vendors, and assesses the outcome of each transaction. A serial first-party fraudster will have a very telling transaction outcome history.
Deep learning. While consortium data and first-party monitoring improve visibility, deep learning can analyze vast datasets to detect fraud patterns, predict transaction outcomes, and automate some aspects of fraud prevention. If you would like to see the in-depth findings of the Fraud.net 2020 Friendly Fraud survey, download it for free.