Publication: Modelos para otorgamiento y seguimiento en la gestión del riesgo de crédito
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Millán Solarte, Julio César
Caicedo Cerezo, Edinson
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Universidad Pablo de Olavide
Abstract
Esta investigación muestra la aplicación y desempeño de tres modelos para la clasificación de solicitantes de créditos: el modelo de análisis discriminante, el de regresión logística y el de redes neuronales; técnicas empleadas por las instituciones financieras en el cálculo del scoring de crédito. Los resultados obtenidos muestran un mejor desempeño del modelo de redes neuronales en comparación con el de regresión logística y análisis discriminante, logrando una tasa de aciertos en la clasificación del 86.9%. Para los tres modelos se emplearon catorce variables que informan sobre las características socioeconómicas del prestatario y sobre las características propias de la operación crediticia. En el ámbito de la gestión financiera, este resultado es importante dado que puede complementarse con el cálculo de la probabilidad de incumplimiento, con los montos expuestos en cada operación de crédito y con la tasa de recuperación de la entidad para establecer el valor de las pérdidas esperadas a nivel individual y a nivel del portafolio de créditos de la entidad.
This research shows the application and performance of three models for the classification of credit applicants: discriminant analysis, logistic regression and neural networks; techniques used by financial institutions for the calculation of credit scoring. The results show a better performance of the neural network model compared to logistic regression and discriminant analysis, achieving a success rate of 86.9% in the classification. For the three models, fourteen variables were used to inform about applicant¿s socioeconomic characteristics and those of the credit operation. In the area of credit risk management, this result is relevant since it can be complemented by the calculation of default probability, the exposure at default and the recovery rate of the entity to establish the value of expected losses at both the individual level and the whole credit portfolio of the entity.
This research shows the application and performance of three models for the classification of credit applicants: discriminant analysis, logistic regression and neural networks; techniques used by financial institutions for the calculation of credit scoring. The results show a better performance of the neural network model compared to logistic regression and discriminant analysis, achieving a success rate of 86.9% in the classification. For the three models, fourteen variables were used to inform about applicant¿s socioeconomic characteristics and those of the credit operation. In the area of credit risk management, this result is relevant since it can be complemented by the calculation of default probability, the exposure at default and the recovery rate of the entity to establish the value of expected losses at both the individual level and the whole credit portfolio of the entity.
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URL del artículo en la web de la Revista: https://www.upo.es/revistas/index.php/RevMetCuant/article/view/2370
Bibliographic reference
Revista de métodos cuantitativos para la economía y la empresa, ISSN-e 1886-516X, Vol. 25, 2018, págs. 23-41




