Systemic financial risk based on analytic hierarchy model and artificial intelligence system

Abstract

In the context of big data, data acquisition methods are more diverse, the amount of data is larger, and the types of data are more diverse. At the same time, the speed of data processing by big data technology will also be greatly improved. Therefore, using big data technology and data mining methods to conduct early warning research on my country's systemic financial risks will eventually become a trend. Based on the analytic hierarchy model and artificial intelligence technology, this paper constructs a systemic financial risk identification model and establishes a practical analytic hierarchy model. Moreover, this paper organically combines the more prominent influencing factors such as swing curve, network structure and unit parameters, and determines the main parameters of the model according to the strength of the influence of each factor on coherence. In addition, this paper proposes a practical coherence recognition method that takes the network topology and unit parameters as considerations and uses the programming program to realize the reading of BPA data, and based on this, analyzes the topology similarity and parameter similarity of the unit. Finally, this paper deeply analyzes the specificity of the equivalence process of the main grid. Through experimental research, we can see that the model constructed in this paper has certain effects.