Abstract
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. In this paper we evaluate the impact of graph-based techniques for credit risk assessment and how different interactions between users within a Super-App provide a new source of information to predict borrower behavior. To this end, five networks are built and two experiments using 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.
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Acknowledgments
Some of the results in this paper were part of the Master thesis of the first author at Universidad de los Andes, under the direction of the last two authors. We would like to thank Jaime Acevedo, Gabriel Suarez and Juan Ráful for their insightful ideas and valuable discussions.
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Roa, L., Rodríguez-Rey, A., Correa-Bahnsen, A., Arboleda, C.V. (2022). Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-82196-8_16
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DOI: https://doi.org/10.1007/978-3-030-82196-8_16
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