Financial Risk Identification and Intelligent Network Monitoring based on BP-SVM Algorithm

Due to the high risk of business activities, enterprises need to identify the financial risks they face, while also understanding the nature of risks and the potential losses that may arise from risk accidents. They need to use these to develop and implement the most effective risk management methods for themselves, minimize potential adverse effects, and maintain the normal operation of the enterprise. The design of the central processing unit (CPU) board for classifying enterprise financial crisis data enables the classification of financial crisis data, facilitating future warnings. Light-emitting diode (LED) indicator lights and passive buzzers can be used to alert financial data in crisis. For the financial information of the enterprise, the financial data collected from sensors is defined, and the financial data of the enterprise is modeled using the back propagation-support vector machine (BP-SVM) algorithm. In the prediction accuracy test of BP-SVM under different training set sizes, when there are 3000 samples in the training set, the average prediction accuracy of BP-SVM is 93.2%, and when there are 2000 samples in the training set, the average prediction accuracy of BP-SVM is 94.6%. The financial risk identification and intelligent network monitoring method based on BP-SVM algorithm in this article has relatively high sensitivity and can adapt to complex environments.