Abstract
With the rapid advancement of the informatization process, enterprise informatization management has received more and more attention. Facing the increasingly complex and changeable social and economic environment, the difficulty of enterprise risk management has gradually increased. How to establish an efficient risk management mechanism for early warning of corporate risks is the goal that companies seek. Traditional statistical analysis can no longer satisfy the processing of massive financial data. Therefore, how to find useful information for the financial risk early warning management of the enterprise from the large amount of financial data information generated by the business activities of the enterprise is a problem that enterprises urgently need to solve at present. The continuous improvement and innovation of data mining technology and the good performance of research and analysis of massive data have made the two closely linked. First, this study introduces the theories of financial risk early warning and data mining technology; second, it introduces the research process of financial risk early warning model and elaborates the three data mining techniques used in this study; then combined with the actual situation of listed companies in my country, it constructs financial risk early warning index system; and finally, 77 listed manufacturing companies and their matching companies that were first processed by ST in 2005‐2007 were used as research samples, based on the financial data of the 2.4 years before being processed by ST and CXISP. It is found that the financial risk early warning model established by data mining technology has strong early warning capabilities. From the perspective of the prediction capabilities of the three models, the closer the time to ST, the higher the accuracy of the prediction; from the perspective of short‐term early warning, the three models have better prediction effects, but from the perspective of long‐term early warning, the prediction effects of neural networks and decision trees are better than logistic regression of statistical analysis; data mining techniques based on knowledge discovery are not only suitable for short‐term early warning but also for longer‐term early warning. Therefore, data mining can be applied to financial risk early warning analysis to achieve the purpose of using data mining technology for decision support.
Highlights
With the rapid development of the market economy and the complex and changeable social and economic environment, business activities cannot be effectively carried out. e financial activities of enterprises are volatile, and the results become unpredictable, which in turn makes the enterprise profitable, or suffer losses, or even go bankrupt. e improper management of corporate financial risks at home and abroad over the years has caused companies to fall into financial crises, or even bankruptcies, which has brought certain resistance to the development and advancement of the entire economy and society
Traditional statistical analysis can no longer satisfy the processing of massive financial data. erefore, how to find useful information for the financial risk early warning management of the enterprise from the large amount of financial data information generated by the business activities of the enterprise is a problem that enterprises urgently need to solve at present. e continuous improvement and innovation of data mining technology and the good performance of research and analysis of massive data have made the two closely linked. erefore, this study uses data mining technology to research and analyze corporate financial risk early warning, which has very important theoretical and practical significance
Conclusions e conclusion is drawn as follows: (1) is study introduces the theories of financial risk early warning and data mining technology
Summary
With the rapid development of the market economy and the complex and changeable social and economic environment, business activities cannot be effectively carried out. e financial activities of enterprises are volatile, and the results become unpredictable, which in turn makes the enterprise profitable, or suffer losses, or even go bankrupt. e improper management of corporate financial risks at home and abroad over the years has caused companies to fall into financial crises, or even bankruptcies, which has brought certain resistance to the development and advancement of the entire economy and society. Journal of Mathematics analyzes the financial risk analysis and early warning research of listed companies and finds that it is still in a process of exploration and improvement in this field. Since the 1980s, Western researchers have begun to use knowledge discovery data mining techniques such as artificial neural networks, expert systems, and genetic algorithms to conduct early warning research on financial risks. Erefore, the use of knowledge discovery data mining technology is an important development trend in the research methods of financial risk early warning problems. Erefore, this study uses data mining technology to research and analyze corporate financial risk early warning, which has very important theoretical and practical significance Management of the enterprise from the large amount of financial data information generated by the business activities of the enterprise is a problem that enterprises urgently need to solve at present. e continuous improvement and innovation of data mining technology and the good performance of research and analysis of massive data have made the two closely linked. erefore, this study uses data mining technology to research and analyze corporate financial risk early warning, which has very important theoretical and practical significance
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