Research on Financial Risk Analysis and Early Warning Based on Data Mining Technology

With the intensification of market competition, enterprises must improve their competitiveness to survive in the fierce environment. Financial management is the most important and economical guarantee to ensure sustainable development and prevent risks. Traditional research methods for enterprise financial risk analysis and early warning mainly include statistical analysis and artificial intelligence models. A comprehensive, accurate and complete enterprise risk analysis model is constructed based on quantitative financial indicators. In addition, the research on enterprise financial risk analysis and early warning is affected by various factors inside and outside the enterprise, and the uncertainty is very high, and the excellent performance of data mining technology in the study of uncertainty theory makes the two closely connected. Therefore, this paper addresses the problems that traditional methods cannot solve, delves into association rule mining and time series analysis prediction models, and combines data mining techniques to analyze enterprise financial risks and help decision-makers develop reasonable investment strategies. At the same time, these algorithms can be applied to the study of enterprise financial risk analysis and crisis early warning. This paper proposes a conceptual hierarchical tree model of corporate financial risk and a financial crisis early warning model for dynamic maintenance of time series.