Accounting Information Disclosure and Financial Crisis Beforehand Warning Based on the Artificial Neural Network

Abstract

With the continuous development of China’s market economy, market competition has become increasingly fierce. In this context, enterprises will face more and more problems. Among them, the financial crisis is undoubtedly the most significant and the biggest problem that threatens the survival and development of enterprises, and it is an unavoidable problem for all enterprises. Irregular accounting information disclosure is an important factor that leads to the financial crisis of enterprises. Because accounting information contains a lot of important financial information within the enterprise, if the information is not disclosed in compliance with the norm, it will cause immeasurable financial crisis to the enterprise. Therefore, it is of great significance to establish an effective financial crisis beforehand warning system and avoid the noncompliant disclosure of accounting information in order to avoid and control the occurrence of financial crisis in enterprises that threaten the survival and development of enterprises. At present, artificial neural network technology is constantly developing and improving with the development of science and technology, and it has been proven that it has remarkable performance for handling nonlinear data, which provides new ideas and technical support for enterprises’ financial crisis beforehand warning. This article is aimed at studying accounting information disclosure and financial crisis beforehand warning based on an artificial neural network. Based on the BP learning algorithm of the artificial neural network, an accounting information disclosure test and financial crisis beforehand warning model were constructed, and with the help of this model, the accounting information disclosure test and financial crisis beforehand warning experiment with H company as an example were carried out, and the conclusions were drawn: the accounting information disclosure test and financial crisis beforehand warning model based on the artificial neural network BP learning algorithm reduce the company’ s financial loss by 8% by reducing the company’ s noncompliant accounting information disclosure rate, and the accuracy rate of the company’ s financial crisis beforehand warning is also increased by 35%.