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.

该论文提出了一种关系图卷积网络方法的框架，用于在超级应用的金融服务中预防欺诈行为；结果表明，当考虑超级应用的替代数据和高连通性中发现的交互时，采用利用这些交互的模型能够将增加价值转化为更好的决策和欺诈检测策略。