Paper
3 February 2023 Enterprise financial risk early-warning method based on data mining
Xiaoying Wang, Zhebin Zhang
Author Affiliations +
Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125110H (2023) https://doi.org/10.1117/12.2660109
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China
Abstract
Because there are many factors affecting the financial risk of enterprises, it is difficult to assess the risk, and the traditional methods are difficult to accurately carry out the risk early warning work. To solve this problem, this paper puts forward the research of enterprise financial risk early warning method based on data mining. First, it constructs a financial risk evaluation system covering the profitability, operation, debt repayment, development and cash flow of the enterprise, and then analyzes the specific situation of each index parameter in the financial risk evaluation system by using BP neural network of data mining technology, and realizes the evaluation of the enterprise's financial risk combined with the actual situation of the market. Then, it makes corresponding early warning according to the evaluation results. The test results show that the accuracy rate of the designed method for different degrees of financial crisis can reach more than 80.0%, which has a reliable early warning effect.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaoying Wang and Zhebin Zhang "Enterprise financial risk early-warning method based on data mining", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125110H (3 February 2023); https://doi.org/10.1117/12.2660109
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data mining

Neural networks

Analytical research

Data modeling

Factor analysis

Reliability

Statistical analysis

Back to Top