Relational Graph Neural Networks for Fraud Detection in a Super-App environment
This addresses fraud detection for digital platforms like Super-Apps, but it is incremental as it applies existing graph neural network methods to a new domain with specific data.
The paper tackled fraud detection in Super-App financial services by proposing a relational graph convolutional network framework that leverages user, device, and credit card interactions, showing added value from alternative data and high connectivity for improved decisions.
Large digital platforms create environments where different types of user interactions are captured, these relationships offer a novel source of information for fraud detection problems. In this paper we propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. To this end, we apply the framework on different heterogeneous graphs of users, devices, and credit cards; and finally use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity, further proofing how they can leverage that into better decisions and fraud detection strategies.