Isolating Fraud Trends
In January 2024, GoFundMe identified a dip in their month-over-month approval rate alongside a spike in fraudulent donation attempts. By visualizing and digging into their aggregated payments data in Peacock by Pagos, they identified commonalities in the bad actors’ transaction attempts, and immediately adjusted their fraud rules to protect their business.
Overview
As an online fundraising platform, GoFundMe fundamentally relies on secure online payment processing. To optimize their processing capabilities, they regularly analyze trends in their payments data, such as approval rate, chargeback volume, average donation values, and so much more. In January 2024, the GoFundMe team identified a significant decrease in approval rate and realized quickly that they were facing a coordinated attack. To avoid any financial loss associated with fraud, they sought to identify commonalities in the fraudulent donation attempts. Lo and behold, they isolated the fraudulent activity to a few key transaction elements they could key off of. Armed with this information, they adjusted their fraud rules to catch and stop any future fraud activity in its tracks.
The Investigation
GoFundMe understands that true payments optimization hinges on a business’ ability to regularly monitor and analyze their payments data. By partnering with Pagos, they have access to the tools they need to view their data in real time across all their processors without contributing significant internal resources to the task. As such, Peacock—Pagos’ payments data visualization service—plays a central role in GoFundMe’s investigation into their fraud concerns.
While monitoring their approval rate broken down across processors for the months of November 2023 through January 2024, they quickly realized they had an issue: for one of their processors, there were material anomalies in processing attributes. Further analysis into the root cause revealed a concerning concentration of a few key data elements; their team then knew where to focus their resources to drive a solution.
Peacock made it possible for them to easily filter their payments data by the problematic attributes and the affected processor. With this level of granular segmentation, they even identified a specific transaction amount associated with the fraudulent donations.
GoFundMe
The Outcome
GoFundMe took what they learned in their payments data and immediately built out a new custom fraud rule to catch and reject any future donation attempts made by this fraud ring. Acting quickly to stop this attack meant GoFundMe could accurately target identifiably bad donation activity and prevent associated downstream costs, while appropriately minimizing impact on legitimate donation activity.