The presence of Super-Apps have changed the way we think about the interactions between users and commerce. It then comes as no surprise that it is also redefining the way banking is done. The paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior. To this end, two experiments with different graph-based methodologies are proposed, the first uses graph based features as input in a classification model and the second uses graph neural networks. Our results show that variables of centrality, behavior of neighboring users and transactionality of a user constituted new forms of knowledge that enhance statistical and financial performance of credit risk models. Furthermore, opportunities are identified for Super-Apps to redefine the definition of credit risk by contemplating all the environment that their platforms entail, leading to a more inclusive financial system.

中文翻译：

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通过图机器学习和超级应用替代数据支持财务包容性

超级应用程序的出现改变了我们思考用户与商业之间交互的方式。因此，毫无疑问，它也重新定义了银行业的完成方式。本文研究了超级应用程序内用户之间的不同交互方式如何提供新的信息源来预测借款人的行为。为此，提出了两个使用不同基于图的方法的实验，第一个使用基于图的特征作为分类模型的输入，第二个使用图神经网络。我们的结果表明，变量的中心性，相邻用户的行为和用户的交易性构成了新的知识形式，这些知识增强了信用风险模型的统计和财务绩效。此外，