Intelligent Financial Risk Assessment for Public Companies via Data Mining Algorithms

This article selects listed machinery enterprises as the research object and explores financial risk, financial auditing theory, and financial auditing methods. It analyzes the characteristics, financial risk types, and causes specific to machinery enterprises. To solve the lack of financial auditing mechanisms in the machinery industry, a financial auditing model is constructed using the backpropagation (BP) neural network model. A sample of 93 enterprises was selected, and SPSS software was used to perform factor analysis on the audit indicators to optimize them, divide the sample interval, and train and test the BP neural network model. Finally, a case analysis of Xingguang Agricultural Machinery was conducted. By using enterprise data to test the model, the results show that the BP neural network model built can accurately audit the financial risks in Xingguang Agricultural Machinery.