Credit ratings of Chinese online loan platforms based on factor scores and K-means clustering algorithm

IF 5.4 2区 管理学 Q1 BUSINESS, FINANCE Journal of Management Science and Engineering Pub Date : 2023-09-01 DOI:10.1016/j.jmse.2022.12.003
Rongda Chen , Shengnan Wang , Zhenghao Zhu , Jingjing Yu , Chao Dang
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引用次数: 1

Abstract

The rapid development of Chinese online loan platforms (OLPs), as well as their risks, has attracted widespread attention, increasing the demand for a complete credit rating mechanism. The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators, including platform transaction volume and average expected rate of return. We also consider two qualitative indicators of online loan background, namely platform background and guarantee mode, that reflect Chinese characteristics. Subsequently, a factor analysis was conducted to reduce the 14 indicators’ dimensions. The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor, fund dispersion factor, security factor, and profitability factor. Finally, a K-means clustering algorithm was employed to cluster the factor scores of each OLP, thereby obtaining credit rating results. The empirical results indicate that the proposed machine learning–based credit rating method effectively provides early warnings of problem platforms, yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China, namely, Wangdaitianyan and Wangdaizhijia.

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基于因子得分和K-means聚类算法的中国网络贷款平台信用评级
中国网络贷款平台(olp)的快速发展及其风险引起了广泛关注,增加了对完整信用评级机制的需求。本研究建立了一个针对国内130家主流网络贷款平台的信用评级指标体系,该体系结合了类似商业银行信用评级指标的12个网络贷款业务量化指标,包括平台交易量和平均预期收益率。我们还考虑了体现中国特色的网贷背景的两个定性指标,即平台背景和担保模式。随后进行因子分析,对14个指标的维度进行降维。将所得旋转分量矩阵中评级指标的载荷细化为OLP运营规模因子、资金分散因子、安全因子和盈利因子。最后,采用K-means聚类算法对每个OLP的因子得分进行聚类,从而得到信用评级结果。实证结果表明,本文提出的基于机器学习的信用评级方法能够有效地对问题平台进行预警,其信用评级的准确性高于国内两大主流网贷评级网站网贷天言和网贷之家。
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来源期刊
Journal of Management Science and Engineering
Journal of Management Science and Engineering Engineering-Engineering (miscellaneous)
CiteScore
9.30
自引率
3.00%
发文量
37
审稿时长
108 days
期刊介绍: The Journal of Engineering and Applied Science (JEAS) is the official journal of the Faculty of Engineering, Cairo University (CUFE), Egypt, established in 1816. The Journal of Engineering and Applied Science publishes fundamental and applied research articles and reviews spanning different areas of engineering disciplines, applications, and interdisciplinary topics.
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