Guzman-Castillo, StefaniaGarizabalo-Davila, ClaudiaAlvear-Montoya, Luis GuillermoGatica, GustavoRodriguez-Heraz, Jaiver DarioMedina-Tovar, Freddy AlfonsoAndrade-Nieves, Sheyla Tatiana2024-04-162024-04-162023-03Procedia 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 1897121877-0509https://repositorio.unab.cl/handle/ria/56059Indexación: Scopus.Credit 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.enCost-effectivenessCcredit riskDisbursementDiscriminant analysisFinancial entitiesArtículoCC BY-NC-ND 4.0 DEED Attribution-NonCommercial-NoDerivs 4.0 International10.1016/j.procs.2023.03.127