Journal of Economics, Finance and Administrative Science

URI permanente para esta comunidadhttps://hdl.handle.net/20.500.12640/4090

La Journal of Economics, Finance and Administrative Science (JEFAS), de la Universidad ESAN, es una publicación académica de acceso abierto que presenta investigaciones revisadas por pares en administración, economía y finanzas, con un enfoque en el contexto latinoamericano e iberoamericano. Fundada en 1992 como Cuadernos de Difusión, en 2009 cambió de nombre a su actual denominación como JEFAS. Ha evolucionado en colaboración con importantes editoriales, como Elsevier y actualmente Emerald Publishing. La revista publica investigaciones de alta calidad sin costo para los autores, con el respaldo de ESAN y su compromiso con la difusión del conocimiento científico y académico, y la práctica gerencial.

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Autor: search.filters.author.Kamimura, Elias ShoheiTema: search.filters.subject.Calificación crediticia

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    A recent review on optimisation methods applied to credit scoring models
    (Universidad ESAN. ESAN Ediciones, 2023-12-11) Kamimura, Elias Shohei; Pinto, Anderson Rogerio Faia; Nagano, Marcelo Seido
    Purpose: 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.