Fraudulent Event Detection via Temporal Graph Networks

The development of new objective functions in Neural Architecture Search (NAS) may include temporal and dynamic aspects of the problem to be modeled. In this line, we show the application of a temporal graph neural algorithm for detecting fraud on real data of an online payment platform, which is made up of different events that a user can perform, such as card registration, device registration, bank account registration, and IP registration. A combination of previous information generates different Event-Based Temporal Graphs (ETG) used in constructing the objective function for our neural network. The results show that combining different events effectively impacts AUC and recall, which is directly related to the proportion of fraudulent observations captured, and that there is a direct correlation between better metrics values and the graph density (measured as the proportion between vertices and edges). Few works focus on applying ETGs for fraud detection using real data and its analysis through the characteristics of the different ETGs, as we do in this work. Furthermore, our application works on real data from Latin America, the region with the highest fraud rate worldwide.