Credit Approval System Using Machine Learning: Challenges and Future Directions

Mohammad Fahim Faisal, Mohammad Neyamath Ullah Saqlain, M. Bhuiyan, Mahdi H. Miraz, M. Patwary
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引用次数: 5

Abstract

The applications of machine learning have now reached variety of industries, including banking and financial organisations. While credit approval is a key concern of the banking industry, machine learning is widely regarded as one of the most effective methods for credit approval. In fact, due to significant amount of research being conducted in this domain, enhanced new algorithms and/or approaches are continuously being proposed by the researchers. Therefore, to compare, contrast and synthesise the performance of these machine learning algorithms, this literature survey covered 52 articles since as far back as 2000. We have also recommended the application of fuzziness-based semi-supervised learning, which has never been previously utilised in the credit approval process, as per our survey findings.
使用机器学习的信用审批系统:挑战和未来方向
机器学习的应用现在已经遍及各行各业,包括银行和金融机构。虽然信贷审批是银行业的一个关键问题,但机器学习被广泛认为是信贷审批最有效的方法之一。事实上,由于在这个领域进行了大量的研究,研究人员不断提出增强的新算法和/或方法。因此,为了比较、对比和综合这些机器学习算法的性能,本文献调查涵盖了自2000年以来的52篇文章。根据我们的调查结果,我们还建议应用基于模糊的半监督学习,这在信贷审批过程中从未被使用过。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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