Financial risk monitoring and warning method of listed enterprises based on data mining

To address the issues of low accuracy in traditional methods for enterprise financial data mining, significant discrepancies between financial risk monitoring results and reality, and low accuracy in risk warning, a data mining-based financial risk monitoring and warning method for listed companies was designed. Firstly, grey relational clustering is used to mine financial data of listed companies. Then, factor analysis and fuzzy recognition matrix are combined to identify financial risks of listed companies. Finally, XGboost algorithm is used to divide financial risks of listed companies. Support vector machine is used to build a financial risk warning decision function for listed companies, achieving financial risk monitoring and warning for listed companies. The experimental results show that the financial risk monitoring results of this method are consistent with the true values, and the data mining accuracy can reach up to 98.23%, with a risk warning accuracy of over 95%. It has a good effect on enterprise financial risk monitoring and warning, and has high application value.