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Modelo de puntuación crediticia para tarjeta de crédito en México: una aproximación logística

Author

Listed:
  • Marco Antonio Reyes Morales

    (UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO)

  • Magnolia Miriam Sosa Castro

    (Universidad Nacional Autónoma de México)

Abstract

El riesgo de crédito es una de las principales preocupaciones de los organismos de supervisión y regulación financiera, así como de las instituciones bancarias. Así, se propone un modelo de puntuación crediticia basado en una regresión logística, para analizar la probabilidad de incumplimiento por segmentos de una cartera de clientes de tarjeta de crédito de una institución mexicana. Los resultados muestran que el modelo propuesto tiene un alto nivel de predictibilidad y de estabilidad, tanto fuera como dentro del periodo de modelado, la comprobación de monotonicidad, también asegura que el modelo tenga un alto nivel de precisión. La originalidad subyace en que existen escasos estudios sobre modelos de puntuación crediticia para México, el resultado del modelo tiene alto nivel de precisión y arroja como resultado una tabla de puntuación de fácil interpretación para el personal bancario. Se concluye que el modelo es confiable y con alto nivel de ajuste.

Suggested Citation

  • Marco Antonio Reyes Morales & Magnolia Miriam Sosa Castro, 2022. "Modelo de puntuación crediticia para tarjeta de crédito en México: una aproximación logística," Ensayos Revista de Economía, Universidad Autónoma de Nuevo León, vol. 41(1), pages 17-52, May.
    • Handle: RePEc:ere:journl:v:41:y:2022:i:1:id:231
    DOI: 10.29105/ensayos41.1-2
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    References listed on IDEAS

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    1. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
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