E-commerce merchants know that, despite its affable name, ‘friendly fraud’ is much less friendly than it is frustrating. It’s long been something that online businesses have had to account for and build into fraud prevention strategies, but the global pandemic has driven some companies’ chargeback rates to historically high levels—leaving trust and safety teams with piles of paperwork to work through and a greater risk of incurring penalties, fees, and other losses.

Changing markets, rising chargebacks

In a recent webinar, Sift’s Michelle Arguelles, Sr. Product Manager, and Trust and Safety Architect, Brittany Allen, explained and explored how merchants can protect their businesses against friendly fraud using holistic prevention strategies and carefully crafted dispute responses. This full-view approach proved effective long before shelter-in-place orders ignited the internet economy, but can be particularly useful in the wake of COVID-19. But before digging into the tactics, Allen notes, it’s necessary to understand the context surrounding friendly fraud and the disrupted online marketplace merchants are now operating in—and to do that, we have to turn to the data. 

Sift’s E-Commerce Fraud Tracker uses a 7-day moving average of our global network data to highlight the week-over-week fluctuations taking place across the pandemic-era marketplace. Traffic and transaction volumes have changed significantly across all verticals in the past two-and-a-half months, and friendly fraud has become noticeably more frequent. The spike comes down to two likely culprits: plans getting cancelled due to social distancing requirements, and sudden financial hardship resulting from loss of income sources. 


This new reality means that merchants need an effective internal dispute process in place now more than ever—because despite the light now showing at the end of the quarantine tunnel, e-commerce is nowhere near returning to business as usual.

Creating a friendly fraud “MFA”: Identity, data, and story

When someone commits friendly fraud, they’re trying to get a refund for a product or service they actually received and/or used. And, because we leave such detailed footprints of our online activities every time we interact with a site, there’s a lot of data available to merchants to fight those illegitimate chargebacks—so Allen suggests developing an “internal multi-factor authentication process” that mirrors what many online merchants do anyway to verify customer legitimacy. This information can then be used when trust and safety teams go through the process of disputing chargebacks. Here’s how it works:  

1. Start with identity. In other words, confirm whether the customer logged into an established account with credentials they’ve used before, paid with a known card or method, submitted known billing and mailing address details, or made the purchase using a known device or IP address.

2. Use internal data. As a merchant, you have access to historical details surrounding how this customer has previously interacted with your site—like whether or not they logged in with SSO, what their purchase history looks like, or if they used a unique promo code. You can even dig deeper into that history to check the chargeback’s logic, i.e., have they recently bought a plane ticket and a seating upgrade, but only filed a dispute on the ticket? You should also look for any records, such as proof of download or proof of delivery, as well as any contradictory communications on the part of the customer, to bolster your evidence. 

3. Map out the story of what has occurred. The most important task on the plate of any trust and safety analyst filing a chargeback dispute is to create a clear, concise, and consumable story that explains what’s really going on. Using the relevant information you’ve now collected about the person’s identity and past behavior, write out the chronological events with specific supporting points and evidence, and accompanied by visuals wherever possible. This adds clarity and efficiency to the process, and provides merchants with the compelling evidence necessary to win disputes and recoup revenue.

Extracting evidence using the Sift Console 

For Sift customers, says Michelle Arguelles, it’s especially easy to surface compelling evidence using the platform’s web-based Console. Fraud teams of all sizes can use it to investigate users and get the right evidence for disputes, automate action when chargebacks are received, and monitor chargeback rates with real-time insights. 

Screenshot from the Sift Console

When it comes to fighting friendly fraud, it’s the User Details page of the Sift Console that provides a wealth of data points necessary to create an airtight “internal MFA.” It tells the story behind how and when transactions are considered risky or fraudulent, and can be downloaded as a PDF or selectively screenshotted depending on the evidence you need. For example, you can use the Order History card to prove a customer has placed multiple orders that have not been charged back. You can also dive into orders for more details. The Activity tab is another great resource, providing a complete timeline of user activity, so you can prove their longevity as a customer on your platform.

In addition to providing evidence for disputes, the Console also has reporting features to help you keep track of your performance. Sift Insights is an all-time, real-time reporting tool that gives you transparency into your chargeback rates over time and your performance with disputes you’ve pursued. With to-the-point, detailed information in hand, merchants can successfully dispute more illegitimate chargebacks—ultimately lowering the risk of penalties and losses, giving analysts more time to focus on true fraud, and enabling your business to drive growth. 

Watch Friendly Fraud: How Trust & Safety Experts Win Disputes to see the full Sift Console demo and learn more about optimizing your friendly fraud strategy.

Related topics

card-not-present fraud

chargeback disputes


digital fraud

digital transactions

Digital Trust and Safety

dynamic friction

false positives


fraud management

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