Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/195396
Authors: 
Millán Solarte, Julio César
Cerezo, Edinson Caicedo
Year of Publication: 
2018
Citation: 
[Journal:] Revista de Métodos Cuantitativos para la Economía y la Empresa [ISSN:] 1886-516X [Volume:] 25 [Year:] 2018 [Pages:] 23-41
Abstract (Translated): 
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.
Subjects: 
credit scoring
credit risk
default probability
discriminant analysis
logistic regression
neural networks
JEL: 
C14
C45
C51
D14
Creative Commons License: 
https://creativecommons.org/licenses/by-sa/4.0/
Document Type: 
Article
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