<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the process of accelerating enterprise development in China, enterprises are prone to financial management crises while grasping development opportunities. This paper is oriented towards enterprise financial management data and proposes an adaptive neuro-fuzzy inference system to reason about risky data. The ANFIS model is integrated into K-mean clustering to provide early warning of enterprise risk by dividing different financial management risk indicators. The validation method uses both the comparison of similar algorithms and the practical test of A enterprise. Compared to other analysis models, the accuracy of the early warning model constructed by the K-Means algorithm is as high as 99.64%, which is suitable for enterprise financial management early warning. Using the model built in this paper to cluster the types of enterprise financial risk, the second cluster centers clusters of enterprises in financial difficulties, a number of indicators show negative results, and the net sales interest rate is as low as −94.23. This indicator is applied to the financial crisis of Company A’s financial situation analysis and found to be consistent with the characteristics of the second cluster center. This verifies the accuracy of the financial early warning model constructed in this paper.</p>
</abstract>

In the process of accelerating enterprise development in China, enterprises are prone to financial management crises while grasping development opportunities. This paper is oriented towards enterprise financial management data and proposes an adaptive neuro-fuzzy inference system to reason about risky data. The ANFIS model is integrated into K-mean clustering to provide early warning of enterprise risk by dividing different financial management risk indicators. The validation method uses both the comparison of similar algorithms and the practical test of A enterprise. Compared to other analysis models, the accuracy of the early warning model constructed by the K-Means algorithm is as high as 99.64%, which is suitable for enterprise financial management early warning. Using the model built in this paper to cluster the types of enterprise financial risk, the second cluster centers clusters of enterprises in financial difficulties, a number of indicators show negative results, and the net sales interest rate is as low as −94.23. This indicator is applied to the financial crisis of Company A’s financial situation analysis and found to be consistent with the characteristics of the second cluster center. This verifies the accuracy of the financial early warning model constructed in this paper.

Research on the Application of Data Mining Techniques in Early Warning Models for Financial Management

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the process of accelerating enterprise development in China, enterprises are prone to financial management crises while grasping development opportunities....

Research on the Application of Data Mining Techniques in Early Warning Models for Financial Management

Publicado en línea: 03 jun 2024

Páginas: -

Recibido: 07 mar 2024

Aceptado: 09 may 2024

DOI: https://doi.org/10.2478/amns-2024-1301

Palabras clave
ANFIS model, K-mean clustering, Risk early warning, Financial management

© 2024 Wei Su, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Application of Data Mining Techniques in Early Warning Models for Financial Management

Publicado en línea: 03 jun 2024

Páginas: -

Recibido: 07 mar 2024

Aceptado: 09 may 2024

DOI: https://doi.org/10.2478/amns-2024-1301

Palabras claveANFIS model, K-mean clustering, Risk early warning, Financial management

© 2024 Wei Su, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
ANFIS model, K-mean clustering, Risk early warning, Financial management