Financial risk early warning method for modern manufacturing enterprises based on RBF neural network

Yanyan Cao

International Journal of Manufacturing Technology and Management2025https://doi.org/10.1504/ijmtm.2025.145936article
AJG 1
Weight
0.37

What the paper says

To detect potential financial risks in advance, reduce the rate of missed and false alarms in risk warning, and improve the accuracy of warning, a modern manufacturing enterprise financial risk warning method based on RBF neural network is proposed. Firstly, network coding methods are used to collect financial data such as total asset turnover rate, current ratio, and net profit margin of modern manufacturing enterprises. Secondly, the K-nearest neighbour method is used to remove outliers from the above data to improve the accuracy of risk warning results. Finally, based on the financial data of modern manufacturing enterprises and the financial risk warning results, a financial risk warning model is constructed using RBF neural network to achieve financial risk warning. The research results indicate that the method has low false positives and false positives rate, and a high F1 value, which is beneficial for improving the accuracy and effectiveness of enterprise management decisions.

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https://doi.org/https://doi.org/10.1504/ijmtm.2025.145936

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@article{yanyan2025,
  title        = {{Financial risk early warning method for modern manufacturing enterprises based on RBF neural network}},
  author       = {Yanyan Cao},
  journal      = {International Journal of Manufacturing Technology and Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijmtm.2025.145936},
}

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Financial risk early warning method for modern manufacturing enterprises based on RBF neural network

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.