A recent review on optimisation methods applied to credit scoring models

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dc.contributor.authorKamimura, Elias Shohei
dc.contributor.authorPinto, Anderson Rogerio Faia
dc.contributor.authorNagano, Marcelo Seido
dc.date.accessioned2024-02-01T22:21:16Z
dc.date.accessioned2024-03-18T15:49:38Z
dc.date.available2024-02-01T22:21:16Z
dc.date.available2024-03-18T15:49:38Z
dc.date.issued2023-12-11
dc.description.abstractPurpose: This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs). Design/methodology/approach: The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs). Findings: The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs. Practical implications: The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs. Originality/value: The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.en_EN
dc.identifier.citationKamimura, E. S., Pinto, A. R. F., & Nagano, M. S. (2023). A recent review on optimisation methods applied to credit scoring models. Journal of Economics, Finance and Administrative Science, 28(56), 352–371. https://doi.org/10.1108/JEFAS-09-2021-0193
dc.identifier.doihttps://doi.org/10.1108/JEFAS-09-2021-0193
dc.identifier.urihttps://hdl.handle.net/20.500.12640/3684
dc.language.isoeng
dc.publisherUniversidad ESAN. ESAN Ediciones
dc.publisher.countryPE
dc.relation.ispartofurn:issn:2218-0648
dc.relation.urihttps://revistas.esan.edu.pe/index.php/jefas/article/view/691/558
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectCredit scoringen_EN
dc.subjectLiterature reviewen_EN
dc.subjectRisk managementen_EN
dc.subjectOptimization methodsen_EN
dc.subjectCalificación crediticiaes_ES
dc.subjectRevisión de literaturaes_ES
dc.subjectGestión de riesgoses_ES
dc.subjectMétodos de optimizaciónes_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#5.02.04
dc.titleA recent review on optimisation methods applied to credit scoring modelsen_EN
dc.typeinfo:eu-repo/semantics/article
dc.type.otherArtículo
dc.type.versioninfo:eu-repo/semantics/publishedVersion
oaire.citation.endPage371
oaire.citation.issue56
oaire.citation.startPage352
oaire.citation.titleJournal of Economics, Finance and Administrative Science
oaire.citation.volume28
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JEFAS Vol. 28 Nº 56 (2023)