Research on the Construction of International Financial Crisis Early Warning Model Based on XGBoost Algorithm

With the in-depth development of economic globalization, the links between financial markets are getting closer and closer, and financial systemic risks are also characterized by diversity, complexity and extensiveness. How to establish a comprehensive early warning mechanism for financial systemic risks is an important topic in modern financial risk management research. However, the traditional financial crisis early warning methods mostly rely on macroeconomic indicators and linear econometric models, which have the limitations of insufficient fitting and low prediction accuracy, and are difficult to meet the current practical application needs. In view of this problem, this paper will deeply explore the application feasibility of artificial intelligence and machine learning technology in this field, and propose an international financial crisis early warning model based on XGBoost algorithm, in order to provide new ideas and methods for international financial crisis early warning. Practice has proved that the financial crisis early warning model based on XGBoost algorithm's efficient parallel processing ability and large-scale data processing ability, combined with financial market data, has achieved better prediction accuracy in practical tests and will provide strong support for financial supervision and decision-making.