An Early Control Algorithm of Corporate Financial Risk Using Artificial Neural Networks

Abstract

Based on DL theory, this paper discusses and studies the early warning of enterprise financial risks in detail. And put forward a new enterprise financial risk early-warning model. The purpose is to enable enterprises to better analyze the changing trend of financial data, make correct decisions by managers and investors of enterprises, and promote the stable development of national economy and enterprises. This model is based on the early-warning theory of enterprises, based on the financial statements, business plans, and other relevant accounting information of enterprises, using accounting, finance, and marketing theories, adopting the methods of ratio analysis, comparative analysis, factor analysis, etc., to warn the financial risks of enterprises. This paper uses a lot of data to train the parameters of the DL financial early-warning model and then verifies the established financial early-warning model. In order to verify the reliability of this model, this model is compared with other two financial early-warning models. The results show that the prediction accuracy of this model is as high as 94%, which is 8~15% higher than that of other models. In this paper, the DL method has been applied to financial risk early warning and achieved good results. It has certain theoretical and practical significance in the field of enterprise financial early warning.