Research on the Risk of Social Stability of Enterprise Credit Supervision Mechanism Based on Big Data

IF 3.6 3区 管理学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Tao Meng, Qi Li, Zheng Dong, Feifei Zhao
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引用次数: 10

Abstract

The study aims to establish a platform-based enterprise credit supervision mechanism, and combined with big data, accurately evaluate the credit assets of enterprises under the influence of social stability risk, and improve the ability of enterprises to deal with risks. Using descriptive statistical methods, the study shows that most local enterprises exist in the form of micro loans, which promotes the development of local economy to a certain extent, but it is a vicious cycle of economic development; The overall prediction accuracy of the single enterprise risk assessment model under the influence of social stability risk is 65%. Compared with the single algorithm, the prediction accuracy of the integrated algorithm model is significantly improved, and the prediction accuracy can reach 83.5%, the standard deviation of data prediction is small, and the stability of the model is high.
基于大数据的企业信用监管机制社会稳定风险研究
本研究旨在建立基于平台的企业信用监管机制,结合大数据,准确评估企业在社会稳定风险影响下的信用资产,提高企业应对风险的能力。采用描述性统计方法,研究表明,大多数地方企业以小额贷款的形式存在,在一定程度上促进了地方经济的发展,但这是一种经济发展的恶性循环;社会稳定风险影响下的单一企业风险评估模型总体预测准确率为65%。与单一算法相比,集成算法模型的预测精度显著提高,预测精度可达83.5%,数据预测标准差小,模型稳定性高。
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来源期刊
Journal of Organizational and End User Computing
Journal of Organizational and End User Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
6.00
自引率
9.20%
发文量
77
期刊介绍: The Journal of Organizational and End User Computing (JOEUC) provides a forum to information technology educators, researchers, and practitioners to advance the practice and understanding of organizational and end user computing. The journal features a major emphasis on how to increase organizational and end user productivity and performance, and how to achieve organizational strategic and competitive advantage. JOEUC publishes full-length research manuscripts, insightful research and practice notes, and case studies from all areas of organizational and end user computing that are selected after a rigorous blind review by experts in the field.
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