IDEAS home Printed from https://ideas.repec.org/a/ids/ijmtma/v39y2025i3-4-5p183-194.html
   My bibliography  Save this article

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

Author

Listed:
  • Yanyan Cao

Abstract

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.

Suggested Citation

  • Yanyan Cao, 2025. "Financial risk early warning method for modern manufacturing enterprises based on RBF neural network," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 39(3/4/5), pages 183-194.
    • Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:183-194
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=145936
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:183-194. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=21 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.