Credit risk scoring model based on the discriminant analysis technique

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dc.contributor.authorGuzman-Castillo, Stefania
dc.contributor.authorGarizabalo-Davila, Claudia
dc.contributor.authorAlvear-Montoya, Luis Guillermo
dc.contributor.authorGatica, Gustavo
dc.contributor.authorRodriguez-Heraz, Jaiver Dario
dc.contributor.authorMedina-Tovar, Freddy Alfonso
dc.contributor.authorAndrade-Nieves, Sheyla Tatiana
dc.date.accessioned2024-04-16T21:33:21Z
dc.date.available2024-04-16T21:33:21Z
dc.date.issued2023-03
dc.descriptionIndexación: Scopus.
dc.description.abstractCredit risk models are vitally important for organizations whose corporate purpose is to operate profitably in the loan or credit business. Technological developments have enabled the application of different statistical techniques to create functions that assist in measuring, and consequently in managing, exposure to credit risk; however, these models must be periodically reassessed and optimized to ensure that they fulfill their objectives. This study addresses problems that have been observed in the model for reading the credit history of customers of a company in the real sector, contributing to the design of a risk-scoring model using the discriminant analysis technique. © 2023 Elsevier B.V.. All rights reserved.
dc.description.urihttps://www-sciencedirect-com.recursosbiblioteca.unab.cl/science/article/pii/S1877050923006622?via%3Dihub
dc.identifier.citationProcedia Computer Science. Volume 220, Pages 928 - 933. 2023 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023. Leuven. 15 March 2023through 17 March 2023. Code 189712
dc.identifier.doi10.1016/j.procs.2023.03.127
dc.identifier.issn1877-0509
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/56059
dc.language.isoen
dc.publisherElsevier B.V.
dc.rights.licenseCC BY-NC-ND 4.0 DEED Attribution-NonCommercial-NoDerivs 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCost-effectiveness
dc.subjectCcredit risk
dc.subjectDisbursement
dc.subjectDiscriminant analysis
dc.subjectFinancial entities
dc.titleCredit risk scoring model based on the discriminant analysis technique
dc.typeArtículo
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