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.

翻译：大型数字平台为捕捉不同类型的用户互动创造了环境,这些关系为发现欺诈问题提供了新的信息来源。在本文件中,我们提出了一个超级App金融服务中防止欺诈行为的关联图变动网络方法框架。为此,我们应用了不同不同用户、装置和信用卡的多式图表框架;最后,使用图形神经网络的可解释性算法来确定与用户分类任务最重要的关系。我们的结果显示,在考虑利用超级App替代数据及其高连通性中发现的互动的模型时,有附加价值,进一步证明他们如何利用这些模型来做出更好的决定和发现欺诈的战略。