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.
Towards Federated Graph Learning for Collaborative Financial Crimes Detection
IBM T.J. Watson Research Center（IBM T.J. 沃森研究中心）；IBM Research Almaden（IBM阿尔马登研究院）；IBM Research Tokyo（IBM东京研究院）；IBM Global Business Services（IBM全球企业咨询服务部）
Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms
University of Houston（休斯顿大学）；University of California, Irvine（加州大学欧文分校）；University of California San Diego（加州大学圣地亚哥分校）；Southwest Jiaotong University（西南交通大学）；University of Science and Technology of China（中国科学技术大学）；Northern Arizona University（北亚利桑那大学）
