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dc.contributor.authorAsencios Casimiro, Rodrigo Andre
dc.contributor.authorAsencios, Christian
dc.contributor.authorRamos Ponce, Oscar Efrain
dc.contributor.otherRamos Ponce, Oscar Efrain
dc.date.accessioned2023-03-21T13:48:36Z
dc.date.available2023-03-21T13:48:36Z
dc.date.issued2023
dc.identifier.citationAsencios, R., Asencios, C. & Gutiérrez-Cárdenas, J. (2023). Profit scoring for credit unions using the multilayer perceptron, XGBoost and TabNet algorithms: Evidence from Peru. Communications in Computer and Information Science, 213. https://doi.org/10.1016/j.eswa.2022.119201es_PE
dc.identifier.issn0957-4174
dc.identifier.urihttps://hdl.handle.net/20.500.12724/17928
dc.descriptionIndexado en Scopuses_PE
dc.description.abstractCredit unions are growing microfinance institutions that base their lending decisions on the judgment of their credit analysts. Therefore, the purpose of this paper is to design 6 profit scoring models, capable of predicting the Internal Rate of Return (IRR) of credit applications, using the multilayer perceptron, XGBoost and TabNet algorithms and thus serve as a support tool for the credit analyst. For this purpose, the least correlated and most independent features were selected from the dataset coming from a Peruvian credit union and composed of 36 402 observations. Then, the hyperparameters of all algorithms were tuned. Finally, the profit scoring models that considered only the selected features were compared to which considered all features. As results, it was obtained that the most significant features that determine the IRR of a loan are the effective monthly interest rate and the member's maximum or average days delinquent. The results obtained from the performance evaluation of the profit scoring models suggested the XGBoost as the best algorithm. In addition, the model that used the XGBoost algorithm and considered all the features had the best performance.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherElsevieres_PE
dc.relation.ispartofurn:issn: 0957-4174
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceRepositorio Institucional - Ulimaes_PE
dc.sourceUniversidad de Limaes_PE
dc.subjectCooperativas de créditoes_PE
dc.subjectAnálisis del créditoes_PE
dc.subjectAlgoritmoses_PE
dc.subjectCredit unionses_PE
dc.subjectCredit analysises_PE
dc.subjectAlgorithmses_PE
dc.subjectPerúes_PE
dc.subject.classificationIngeniería de sistemas / Diseño y métodoses_PE
dc.titleProfit scoring for credit unions using the multilayer perceptron, XGBoost and TabNet algorithms: Evidence from Perues_PE
dc.typeinfo:eu-repo/semantics/article
dc.type.otherArtículo en Scopuses_PE
ulima.areas.lineasdeinvestigacionDesarrollo empresarial / Finanzas y proyectos de inversiónes_PE
dc.identifier.journalExpert Systems with Applicationses_PE
dc.publisher.countryGBes_PE
dc.description.peer-reviewRevisión por pareses_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2022.119201
dc.type.versioninfo:eu-repo/semantics/publishedVersion
ulima.lineadeinvestigacionDesarrollo empresarial / Finanzas y proyectos de inversiónes_PE
dc.contributor.studentAsencios Casimiro, Rodrigo Andre (Ingeniería de Sistemas)
ulima.cat009
ulima.autor.afiliacionDepartment of Engineering and Architecture, Universidad de Limaes_PE
ulima.autor.carreraIngeniería de Sistemases_PE
dc.identifier.isni0000000121541816


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