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Terán-Bustamante, Antonia
Crédito hipotecario: un modelo predictivo de discriminación de riesgo
What Drives Profit Income in Mexico’s Main Banks? Evidence Using Machine Learning
2023
,
González-Rossano, Carlos
,
De la Torre Díaz, Lorena
,
Terán-Bustamante, Antonia
Diversos estudios demuestran la relación entre el acceso a la vivienda y la superación de la pobreza. Sin embargo, existe un rezago en el acceso a la vivienda digna en México y la falta de historial crediticio es una limitante para el acceso a créditos bancarios. El objetivo de la presente investigación es analizar los criterios de selección de crédito hipotecario y proponer un modelo de gestión de riesgos que permita a la banca financiar a un mayor número de personas en la adquisición o mejora de su vivienda. La estrategia metodológica se basa en técnicas de aprendizaje automático apoyadas en la ciencia de datos para crear un modelo predictivo del cumplimiento del crédito basado en características individuales. Los resultados muestran un modelo predictivo de discriminación de riesgo con una confiabilidad del 85% para créditos a la vivienda, lo cual permite ampliar la base potencial de personas susceptibles de acceder a financiamiento hipotecario. El derecho a una vivienda digna presenta un rezago importante en el país y hasta ahora los bancos al proponer un modelo predictivo de selección de riesgo hipotecario se da respuesta a la pregunta de investigación que refiere a las acciones que puede ejecutar la banca para resolver el problema de falta de acceso a vivienda digna. Los bancos pueden establecer sus criterios de selección de riesgo apoyados en la ciencia y analítica de datos y la aplicación de modelos predictivos de aprendizaje automático utilizando su amplia base de datos histórica.© Revista Venezolana de Gerencia
2023
,
González-Rossano, Carlos
,
Terán-Bustamante, Antonia
,
Velázquez-Salazar, Marisol
,
Martínez Velasco, Antonieta Teodora
Historically, the banking system has been critical to the development of economies by addressing funds efficiently—from customer savings and investors to the productive activities of people and companies, financing consumer goods and current expenses, housing, infrastructure projects and providing liquidity to the market. However, it must be transformed to respond to emerging demands in society for better financial products and services with a positive impact on living conditions and well-being. To achieve this, banks must create economic value—that is to say, banks should create profits in a sustained manner—in order to also create social value and thus generate shared value. The purpose of this study was twofold. The first aim was to identify the main factors that contributed to the majority of Mexican banking profits in the period from 2003 to 2021; the second aim of the study was to provide an innovative metric of banking performance. Using supervised machine learning algorithms and Principal Component Analysis, two prediction models were tested, and two banking performance indices were defined. The findings show that Random Forest is a reliable profit prediction model with a lower mean absolute error between the predicted yearly profit and losses and the actual data. There are no significant ranking position differences between the two performance indices. The first performance index obtained is novel due to its simplicity, since it is built on the basis of five values associated with commercial banking activity. In Mexico, no similar studies have been published. The indicator most widely used by regulators worldwide is the CAMELS index, which is a weighted average of the capital adequacy level, asset quality, management capacity, profitability, liquidity, and sensitivity to market risk. Its scale of 1 to 5 is useful for identifying the robustness and solvency of a bank, but not necessarily its capacity to generate profits. This approach might encourage banks to remain aware of their potential to create shared value and to develop competitive strategies to increase benefits for stakeholders. © 2023 by the authors.