Interview

How to get ahead of the chargeback surge by forecasting your chargeback rate

Monday 6 December 2021 08:16 CET | Editor: Anda Kania | Interview

Kevin Lee, Trust and Safety Architect at Sift: 'First-party misuse or ‘friendly fraud’ accounts for the majority of the chargebacks filed, especially during the pandemic when sudden financial hardships may have pushed people to fraudulently file disputes'


What is the chargebacks state of affairs for this year? Should we expect an increase in these events during the peak season?

Chargebacks are on the rise across the board. We recently analysed data from the Sift global network that consists of more than 70 billion events a month to uncover trends and insights about the state of affairs for chargebacks. Our full findings will be released later this month in our quarterly Digital Trust & Safety Index report, but we found the average number of daily chargeback cases rose by 19% between Q1 2020 and Q1 2021.

As for whether merchants will see an increase in chargebacks during the peak season, I would say no, or at least, not yet. Chargebacks usually hit merchants in January following the holiday shopping season. But will merchants see an increase early in 2022? Absolutely. Our network data revealed a significant increase in overall order volume during the 4-day stretch between Black Friday and Cyber Monday, especially in the online marketplace vertical. Merchants usually relax their defenses during the peak season to accept more orders, so if the order volume is increasing and merchants are allowing more orders through, it’s safe to say they should expect an increase in the new year.

What is the line between legitimate chargebacks and first-party misuse? Do one or the other occur more frequently across the industry?

While some research suggests only 25% of disputes filed are the result of actual fraudulent purchases (i.e. true fraud), consumers recently surveyed by Sift reported nearly double the digital abuse - 42% of respondents who filed disputes did so due to unauthorised purchases made with their payment information. When asked separately, 17% of those who have filed chargebacks admitted to filing a fraud dispute for a transaction that wasn’t actually fraudulent—a practice known as friendly fraud

Although some friendly fraud disputes may simply be a result of forgetfulness, family members making unknown purchases, or misunderstandings of return policies, the practice also provides amateur fraudsters a low-risk way to make a transaction and then falsely file a dispute claiming they never received the product or service.

But, overall, first-party misuse or ‘friendly fraud’ accounts for the majority of the chargebacks filed, especially during the pandemic when sudden financial hardships may have pushed people to fraudulently file disputes.

How can merchants anticipate the chargebacks rate and what third parties they should rely on in this matter?

There’s industry research, like the Digital Trust & Safety Index reports mentioned above, that merchants can look to for guidance, but the best way for merchants to understand their chargeback rate is to analyse their own data. Historical data, i.e. looking at the same period in previous years, can be helpful in anticipating seasonal swings in chargeback rates.

Merchants will also want to figure out their ‘chargeback arrival curve’, which is basically measuring how fast chargebacks are coming in and then using that figure to extrapolate a trend line to determine whether their chargeback rate is going to go high or low.

During the peak season, there may be some changing payment patterns among consumers that can be mistaken for anomalies. So how can merchants avoid blocking good consumers and reduce false positive rates?

The stakes are definitely high during the peak season. One false positive can send a potential customer directly to your competitor or leave a loyal shopper rethinking those loyalties. To reduce false positive rates and customer insult, don’t just focus on one single data point to make a determination, but look at orders holistically. Of course, there will be times when the billing address doesn’t match the shipping address, but that shouldn’t be your only determining factor on whether an order is legitimate or fraudulent. At Sift, we analyse 16,000 unique signals to stop fraud. We transform single data points, such as an email address, into hundreds of signals like similar emails, disposable domains, and email age. These rich signals feed our ensemble of custom, predictive models to help merchants catch more fraud without insulting customers. An unusual payment pattern, on its own, may indicate fraud, but when added to many other data sources, it might not.

Are there any other fraud trends specific to the peak season that merchants should keep an eye out for?

We’re seeing fraudsters taking bigger swings. As transaction volumes increase during the peak season, fraudsters have gotten smarter and more intentional about their attacks, relying on the huge upswings in traffic and transactions to skirt past security measures.

Sift data shows that each attempted fraudulent purchase across ecommerce is now worth an average of 70% more than they were pre-pandemic, increasing in value from USD 416.00 USD in October/November 2019 to USD 710.00 USD. And even more recent data from Black Friday and Cyber Monday show an even larger increase in fraudulent order value. It’s a reminder to merchants that one instance of fraud can be incredibly costly, so they must remain vigilant.

About Kevin Lee

Kevin Lee is a Trust and Safety Architect at Sift who helps customers implement strategies that cross-functionally align risk and revenue programs. Prior to Sift, he has spent the last 14+ years leading various risk, chargeback, spam/scams, and trust and safety organizations at Facebook, Square and Google.

 

About Sift

Sift is the leader in Digital Trust & Safety, empowering digital disruptors to Fortune 500 companies to unlock new revenue without risk. Sift dynamically prevents fraud and abuse through industry-leading technology and expertise, an unrivalled global data network of 70 billion events per month, and a commitment to long-term customer partnerships. Global brands such as Airbnb, Doordash, and Wayfair rely on Sift to gain a competitive advantage in their markets. 


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Keywords: chargebacks, Sift, friendly fraud, fraud prevention, first-party misuse, ecommerce
Categories: Securing Transactions | Digital Identity, Security & Online Fraud
Countries: World
This article is part of category

Securing Transactions