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Redes bayesianas aplicadas a problemas de credit scoring. Una aplicación práctica

Author

Listed:
  • Mauricio Beltrán Pascual

    (Departamento de Economía Aplicada y Estadística, Facultad de Ciencias Económicas y Empresariales, UNED, Madrid, España)

  • Azahara Muñoz Martínez

    (Facultad de Ciencias Empresariales, Universidad Autónoma de Chile, Santiago de Chile, Chile)

  • Ángel Muñoz Alamillos

    (Facultad de Ciencias Empresariales, Universidad Autónoma de Chile, Santiago de Chile, Chile)

Abstract

En este artículo se aborda la forma de construir un clasificador eficiente a través de redes bayesianas utilizadas en la minería de datos y cuya finalidad es conseguir más precisión que otros modelos empleados en los problemas de credit scoring. El enfoque bayesiano, basado en modelos de probabilidad, emplea la teoría de la decisión para el análisis del riesgo eligiendo en cada situación que se presenta la acción que maximiza la utilidad esperada. Usando una muestra de datos bancarios reales se concluye la superior capacidad predictiva de estos modelos respecto a los resultados obtenidos por otros métodos estadísticos paramétricos y no paramétricos.

Suggested Citation

  • Mauricio Beltrán Pascual & Azahara Muñoz Martínez & Ángel Muñoz Alamillos, 2014. "Redes bayesianas aplicadas a problemas de credit scoring. Una aplicación práctica," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 37(104), pages 73-86, Agosto.
    • Handle: RePEc:cud:journl:v:37:y:2014:i:104:p:73-86
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    More about this item

    Keywords

    Redes bayesianas; Manto de Markov; Credit scoring; Curva ROC; Multiclasificadores;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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