Reducción de la brecha del crédito en México en un ambiente de incertidumbre generada por la pandemia COVID-19: Un enfoque de ciencia de datos (machine learning)

Reducing the credit gap in Mexico in an environment of uncertainty generated by the COVID-19 pandemic: A data science approach (machine learning)

Jair Hissarly Rodríguez-García and Francisco Venegas-Martínez

MPRA Paper from University Library of Munich, Germany

Abstract: Resumen El otorgamiento de microcréditos de forma eficiente y transparente a través de plataformas digitales a individuos que desarrollan actividades económicas y que buscan mantener su empleo y el de sus trabajadores y que no tienen acceso al sistema financiero convencional es, sin duda, un problema urgente por resolver en la crisis sanitaria por la que atraviesa actualmente México. La presente investigación desarrolla varios modelos y estrategias de riesgo de crédito que permiten promover la inclusión crediticia en México de manera justa y sostenible en un ambiente de incertidumbre generada por los estragos presentes y esperados por la pandemia COVID-19. Para ello se utiliza el enfoque de ciencia de datos de machine learning, particularmente, se emplean las herramientas: regresión del árbol de decisión, bosques aleatorios, función de base radial, boosting, K-Nearest Neigbor (KNN) y Redes Neuronales. Abstract The efficient and transparent granting of microcredits through digital platforms to people who carry out economic activities and who seek to maintain their employment and that of their workers and who do not have access to the conventional financial system is, without a doubt, an urgent problem be solved in the health crisis that Mexico is going through. This research develops various credit risk models and strategies that allow promoting credit inclusion in Mexico in a fair and sustainable manner in an environment of uncertainty generated by the present and expected ravages of the COVID-19 pandemic. For this, the data science approach of machine learning is used, in particular, the used tools are: decision tree regression, random forests, radial basis function, boosting, K-Nearest Neigbor (KNN), and Neural Networks.

Keywords: riesgo crédito; ciencia de datos; mercados de créditos; instituciones financieras; inclusión financiera. credit risk; data science; credit markets; financial institutions; financial inclusion. (search for similar items in EconPapers)
JEL-codes: G23 (search for similar items in EconPapers)
Date: 2021-01-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fle and nep-mfd
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