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Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data

Author

Listed:
  • Luisa Roa
  • Andr'es Rodr'iguez-Rey
  • Alejandro Correa-Bahnsen
  • Carlos Valencia

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. 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.

Suggested Citation

  • Luisa Roa & Andr'es Rodr'iguez-Rey & Alejandro Correa-Bahnsen & Carlos Valencia, 2021. "Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data," Papers 2102.09974, arXiv.org.
    • Handle: RePEc:arx:papers:2102.09974
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    References listed on IDEAS

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    2. Tobias Berg & Valentin Burg & Ana Gombović & Manju Puri, 2020. "On the Rise of FinTechs: Credit Scoring Using Digital Footprints," The Review of Financial Studies, Society for Financial Studies, vol. 33(7), pages 2845-2897.
    3. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    4. J. Christopher Westland & Tuan Q. Phan & Tianhui Tan, 2018. "Private Information, Credit Risk and Graph Structure in P2P Lending Networks," Papers 1802.10000, arXiv.org.
    5. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    6. Luisa Roa & Alejandro Correa-Bahnsen & Gabriel Suarez & Fernando Cort'es-Tejada & Mar'ia A. Luque & Cristi'an Bravo, 2020. "Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications," Papers 2005.14658, arXiv.org, revised Jan 2021.
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