Bayesian networks applied to credit scoring problems. A practical application [Redes bayesianas aplicadas a problemas de credit scoring. Una aplicación práctica]

dc.contributor.author	Beltrán Pascual M.
dc.contributor.author	Muñoz Martínez A.
dc.contributor.author	Muñoz Alamillos T.
dc.date.accessioned	2020-09-02T22:13:04Z
dc.date.available	2020-09-02T22:13:04Z
dc.date.issued	2014
dc.identifier	10.1016/j.cesjef.2013.07.001
dc.identifier.citation	37, 104, 73-86
dc.identifier.issn	02100266
dc.identifier.uri	https://hdl.handle.net/20.500.12728/3724
dc.description	This paper analyses how to build an efficient classifier across Bayesians networks used in data mining. The purpose of using the Bayesian model is to improve credit scoring accuracy. The Bayesian approach, based on probability models, analyses risk by using the decision theory, yielding as a solution that action that maximizes the expected utility. Expert assessment may be included in the model. To show the superiority of the Bayesian approach, results obtained for real bank data are compared with those obtained with alternative parametric and non-parametric models. © 2013 Asociación Cuadernos de Economía.
dc.language.iso	es
dc.publisher	Asociacion Cuadernos de Economia
dc.subject	Bayesians networks
dc.subject	Credit scoring
dc.subject	Markov blanket
dc.subject	Multiclassifiers
dc.subject	ROC curve
dc.title	Bayesian networks applied to credit scoring problems. A practical application [Redes bayesianas aplicadas a problemas de credit scoring. Una aplicación práctica]
dc.type	Article

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