Research on the Construction of Enterprise Financial Risk Early Warning Model Based on Data Mining Technology

With the rapid rise of the digital economy and the deepening of the globalization process, the market environment in which enterprises are located is increasingly complex and changeable, and the financial risks caused by it are becoming more and more prominent. However, the traditional financial risk early warning methods adopted by enterprises mainly rely on the analysis of financial indicators and simple statistical models, which have some limitations, such as imperfect indicator system, unreasonable model assumptions and insufficient data processing ability, and are difficult to meet the practical application needs of enterprises. In this regard, this paper will analyze the current situation of enterprises, and focus on the feasibility of data mining technology in this field, and put forward a brand-new enterprise financial risk early warning model, aiming at improving the comprehensiveness and accuracy of financial risk identification and early warning. Practice has proved that the whole model is built on the basis of XGBoost integrated learning algorithm, which can respond to the changes of enterprise financial situation in real time, send out risk warning signals in advance, and provide timely decision support for enterprise management. At the same time, it is better than the traditional financial risk early warning method in accuracy and recall, and can provide more accurate and effective financial risk early warning for enterprises.