Friendly fraud occurs when a cardholder makes a purchase and then files a chargeback. Often, the cardholder simply doesn’t recognize the charge. But there are also shoppers who intentionally abuse the chargeback system.
At Riskified, we use the term ‘liar-buyer’ to describe a case of friendly fraud that occurs when a cardholder knowingly takes advantage of the chargeback process. But what makes shoppers turn to this?
In some cases, shoppers may be experiencing buyer’s remorse. In others, it might just be a matter of convenience – preferring to deal with the bank rather than the merchant – for a refund. Ultimately, these cases account for about 20% of all fraud-related losses online merchants incur, or close to $22 billion annually.
A chargeback dispute process is one way that merchants can fight these abuses, though it’s a resource-intensive process. Merchants have to compile extensive and thorough evidence in order to prove to the bank that the transaction was, in fact, authorized by the cardholder. To compound the problem, not having one team or department dedicated to collecting and organizing the relevant details can make building, and winning the case, challenging.
Below I’ll walk through 3 real-life examples of ‘liar-buyer’ cases encountered by our merchants. This blog post will outline how we leverage our technology to curb this type of fraud, and how our automated representment solution can help merchants successfully dispute these claims without straining internal resources.
*Names have been changed for anonymity
The aftermath of a break-up
Sara and her boyfriend were planning a vacation to Italy. Months before the trip, she booked a five-star hotel with her company’s email in order to use the loyalty points affiliated with the company account. The hotel had a no-refund policy once the reservation was made. A few weeks before the trip, Sara and her boyfriend broke up. In a panic over the money she had spent, Sara called her bank to dispute the charge. Since she didn’t use her personal email, she explained that she didn’t recognize the purchase.
When the merchant forwarded us the claim, the company email was in fact what enabled our models to link Sara to the purchase. By using data enrichment tools, the company website was extracted from the email and Sara’s first and last name matched the name listed among other employees on their ‘Meet the team’ page. The country domain of the email also corresponded to the geolocation of where the order was placed, as did the credit card BIN. Lastly, device fingerprinting enabled our system to identify a keyboard language match to the geolocation, which furthered the verification of her true online identity.
More than $1k won back.
The minor change that would cost a fortune
Jon worked as a biology professor at the University of Iowa. Every semester, he attends a conference relevant to his research project. This year, there was a biology conference in Wisconsin. He booked the airline tickets with his credit card as part of a grant-funded budget. After reserving the tickets months in advance, he realized he had booked the returning flights on the wrong date.
When he contacted the airline to change the tickets, he was told that because they were booked at a discounted price, no changes could be made to the reservation. When he asked if he could cancel and rebook with them, he was hit with a huge cancelation fee of almost the price of a new ticket. To avoid these exuberant fees, he filed a chargeback so he could use the money to simply purchase new flights. Because Jon made the reservation with his educational email, our system could link his first and last name to the email. Most importantly, the order was placed with the University of Iowa browser IP, which also matched his email domain.
Almost $2k won back.
A matter of size
When Mary moved away to the University of Pennsylvania, her parents set apart a small allowance for her college expenses. They agreed she could use her father’s credit card to order online, and she regularly did. When December came, Mary asked her parents for a luxury brand winter coat. Mary’s shopping habits were concerning to her parents, especially when she insisted on expensive purchases, but after many arguments, they gave in. Since she wasn’t familiar with the brand, she ordered two different sizes to try on. When Mary received the package, she tore off the tags and wore them each for a few days. When she went to return the larger size, she read the full return policy online and realized that she wouldn’t be able to.
They had a strict return policy that all tags had to be kept intact, and the merchandise in the original packaging, unworn. Upset with his daughter, Mary’s father called the company and reported the charge as fraudulent. With a billing and shipping mismatch, his credit card company accepted the chargeback.
Mary was a frequent online shopper and had a rich track record of orders with the same IP browser, billing, and shipping information. Her LinkedIn profile served as an important data point to identify her as a good customer in our database. Her LinkedIn not only matched the shipping name but listed her as a member of Alpha Chi Omega at Upenn, which enabled our system to link the sorority to her corresponding IP. Due to this, we approved all of her transactions in the past, and no chargebacks ensued. To ultimately prove the legitimacy of the order, Mary had signed a proof of delivery from Fedex, in which her signature corresponded to the shipping name.
More than $2k won back.
Automated chargeback representment by Riskified
In addition to reimbursing merchants for the approved order amount, shipping costs, and chargeback processing fees, we offer representment to help merchants battle friendly fraud with our automated chargeback representment solution. Studies show that merchants spend as much as 20% of their operational budget on fighting chargebacks. With automated chargeback representment, merchants can focus on driving revenue, while we handle and collect the evidence to dispute chargebacks we believe were placed by the rightful cardholder. Our typical dispute win rates are 20-40%.
Being able to recoup these charges allows us to take on greater risk and approve more orders upfront. Furthermore, liar buyer incidents are often not a one-time occurrence. In fact, nearly half of abusers file another illegitimate chargeback within 90 days. By challenging chargebacks, representment can help disincentivize these shoppers from coming back again and again. To learn more about representment, contact us: firstname.lastname@example.org