A Temporal Graph Network Algorithm for Detecting Fraudulent Transactions on Online Payment Platforms

A temporal graph network (TGN) algorithm is introduced to identify fraudulent activities within a digital platform. The central premise is that digital transactions can be modeled via a graph network where various entities interact. The data used to build an event-based temporal graph (ETG) were sourced from an online payment platform and include details such as users, cards, devices, bank accounts, and features related to all these entities. Based on these data, seven distinct graphs were created; the first three represent individual interaction events (card registration, device registration, and bank account registration), while the remaining four are combinations of these graphs (card–device, card–bank account, device–bank account, and card–device–bank account registration). This approach was adopted to determine if the graph’s structure influenced the detection of fraudulent transactions. The results demonstrate that integrating more interaction events into the graph enhances the metrics, meaning graphs containing more interaction events yield superior fraud detection results than those based on individual events. In addition, the data used in this work correspond to Latin American payment transactions, which is relevant in the context of fraud detection since this region has the highest fraud rate in the world, yet few studies have focused on this issue.