Elias Shohei Kamimura, Anderson Rogério Faia Pinto, M. S. Nagano
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引用次数: 0
Abstract
PurposeThis paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).Design/methodology/approachThe 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).FindingsThe 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 implicationsThe 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/valueThe 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.
目的本文旨在介绍最新的优化方法应用于信用评分模型(csm)的文献综述。设计/方法/方法研究方法采用了基于文献和探索性分析的技术程序。使用Scopus、ScienceDirect和Web of Science数据库进行传统调查。论文的选择和分类分三步进行,仅考虑英语研究和发表在电子期刊上(从2008年到2022年)。该调查最终选择了46份出版物(10份文献综述,36份建议csm)。研究结果表明,csm通常使用财务分析、机器学习、统计技术、运筹学和数据挖掘算法来制定。研究人员使用的主要数据库是银行和加州大学欧文分校。分析确定了csm使用的48种方法,主要是:逻辑回归(13%),朴素贝叶斯(10%)和人工神经网络(7%)。作者得出结论,信用评分研究的进步将需要新的混合方法,能够将大数据和深度学习算法集成到csm中。这些算法应考虑实际问题,考虑提高csm所需的适应水平和性能的实际问题。实际意义本研究的结果可能为csm的应用提供相当大的实际意义。由于其目的是展示优化方法的应用,法律和伦理问题应该更好地适应csm,这是非常可观的。还建议通过改进新的客户服务管理体系,改进以微型和小型公司为重点的分期付款销售计划和商业信贷研究。原创性/价值授信的经济现实使得风险管理成为一个复杂的决策问题,越来越多地得到csm的支持。因此,本文满足了文献中的一个重要差距,以介绍应用于csm的优化方法的最新进展的分析。本文的主要贡献包括介绍了旨在提出更好的csm的研究的最新进展和未来趋势。
期刊介绍:
The Universidad ESAN, with more than 50 years of experience in the higher education field and post graduate studies, desires to contribute to the academic community with the most outstanding pieces of research. We gratefully welcome suggestions and contributions from business areas such as operations, supply chain, economics, finance and administration. We publish twice a year, six articles for each issue.