Financial Risk Monitoring Analysis Based on Integrated SVM Data Stream Classification Algorithm

Abstract

In recent years, with the continuous development of China’s national economy, financial risks have become more and more complex, and present characteristics such as multi-level and complexity. Financial risk monitoring is an important means to prevent and resolve financial systemic crises, which has also become the main method to prevent, control and manage possible problems and potential dangers in the operation of the financial industry. In the traditional statistical classification methods, the data volume is large and unstable, which cannot accurately reflect the specific manifestations of financial risks. Therefore, when classifying data in an integrated SVM system, a combination of statistical vectors and support vector machines can be used to quantify and analyze a large and complex sample set. In this paper, we combine the method of financial risk classification and SVM support vector machine to establish a monitoring and analysis model based on integrated SVM data flow, and study the data integration optimization of financial risk monitoring system.