Statistical Qualification for Approval of Commercial Credits through Generalized Additive Models
This article presents the application of a methodological procedure for the construction of a statistical qualification model for the approval of commercial credits in a public financial institution. In this line, the main aim is to reveal the benefits of using generalized additive models (GAM), whose functional structures contemplate the possible non-linearity of the explanatory variables of credit risk in relation to compliance with the payment obligations of borrowers, compared to linear models like the logit. This topic becomes relevant in view of the need for financial institutions to have the right tools and information management systems that allow them to de-establish strategies to improve the placement of their loan portfolio with clients who can fulfill their agreed obligations within the established deadlines, without incurring partial or total delays; in short, minimizing your credit risk. Additionally, in order to meet the stated need, the methodological procedure is applied through programming in the R software, with which the modeling is easily replicable.
W. Leontief, “Introduction to a theory of the internal structure of functional relationships Econometrica,” Journal of the Econometric Society, pp. 361-373, 1947.
J. Friedman and W. Stuetzle, “Projection pursuit regression,” Journal of the American statistical Association, vol. 76, no. 376, pp. 817-823, 1981.
C. Stone and C. Koo, “Additive splines in statistics,” Proceedings of the American Statistical Association, vol. 45, pp. 48, 1985.
T. Hastie and R. Tibshirani, “Exploring the nature of covariate effects in the proportional hazards model,” Biometrics, pp. 1005-1016, 1990.
M. Müller and W. Härdle, “Exploring credit data,” in Credit Risk, Physica-Verlag HD, pp. 157-173, 2003.
C. Hervás and F. Martínez, “Logistic regression using covariates obtained by product-unit neural network models,” Pattern Recognition, vol. 40, no. 1, pp. 52-64, 2007.
A. Novales, Econometría, 2th ed., Madrid: McGraw-Hill, 1988.
Y. Orgler, “A credit scoring model for commercial loans,” Journal of money, credit and banking, vol. 2, no. 4, pp. 435-445, 1970.
Y. Orgler, “Evaluation of bank consumer loans with credit scoring models,” Tel-Aviv University, Department of Envirnonmental Sciences, 1971.
J. Wiginton, “A note on the comparison of logit and discriminant models of consumer credit behavior,” Journal of Financial and Quantitative Analysis, vol. 15, no. 3, pp. 757-770, 1980.
T. Hastie and R. Tibshirani, Generalized Linear Models (with Discussion), Statistical Science, vol. 1, no. 3, pp. 297-318, 1986.
N. Garrido, “Construcción de un modelo scoring de aprobación para cartera comercial de una institución financiera pública mediante modelos aditivos generalizados,” Master Thesis, Quito, Ecuador, 2019.
E. Steyerberg and F. Harrell, “Internal validation of predictive models: efficiency of some procedures for logistic regression analysis,” Journal of clinical epidemiology, vol. 54, no. 8, pp. 774-781, 2001.
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