Article: RBF neural network model construction for enterprise financial big data analysis Journal: International Journal of Computational Systems Engineering (IJCSYSE) 2026 Vol.10 No.5 pp.1 - 9 Abstract: The study builds a system of financial indicators first, and then uses the fast density peak clustering (FDPC) algorithm and the Adam algorithm to optimise the radial basis function (RBF) network to create a model for predicting financial risk. The results reveal that the initial accuracy of the FDPC Adam RBF model is higher than 60%, and it tends to converge at four iterations, resulting in an accuracy of 95.6%. The FDPC Adam RBF model achieved a minimum value of 0.183 in mean square error (MSE). In summary, it can be seen that the RBF neural network model for enterprise financial big data analysis is significantly better than other common neural network models in terms of computational efficiency and prediction accuracy, making it more suitable for deep analysis of financial data and risk warning. This conclusion provides strong support for the application of advanced artificial intelligence technology in the financial field. Inderscience Publishers - linking academia, business and industry through research

Title: RBF neural network model construction for enterprise financial big data analysis

Authors: Na Feng

Addresses: The Economic and Trade Department, Shijiazhuang College of Applied Technology, Shijiazhuang, 050000, China

Abstract: The study builds a system of financial indicators first, and then uses the fast density peak clustering (FDPC) algorithm and the Adam algorithm to optimise the radial basis function (RBF) network to create a model for predicting financial risk. The results reveal that the initial accuracy of the FDPC Adam RBF model is higher than 60%, and it tends to converge at four iterations, resulting in an accuracy of 95.6%. The FDPC Adam RBF model achieved a minimum value of 0.183 in mean square error (MSE). In summary, it can be seen that the RBF neural network model for enterprise financial big data analysis is significantly better than other common neural network models in terms of computational efficiency and prediction accuracy, making it more suitable for deep analysis of financial data and risk warning. This conclusion provides strong support for the application of advanced artificial intelligence technology in the financial field.

Keywords: financial crisis; financial indicators; radial basis function; RBF; fast density peak clustering; FDPC; Adam.

DOI: 10.1504/IJCSYSE.2026.151608

International Journal of Computational Systems Engineering, 2026 Vol.10 No.5, pp.1 - 9

Received: 08 Jun 2023
Accepted: 14 Mar 2024
Published online: 10 Feb 2026 *

Title: RBF neural network model construction for enterprise financial big data analysis

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