  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.
