Article Preview
TopIntroduction
The rapid development of China’s national economy has led to the proliferation of enterprises. At the same time, the instability of the market environment means enterprises face increasing competitive pressure, and listed companies face even greater financial risks (Zaki, 2017). Finance is the foundation of an enterprise. In particular, for listed companies, once the financial risk of enterprises is out of control, it will lead to a certain financial crisis and even bankruptcy (Fichtner et al., 2017). Therefore, evaluating and predicting the financial risk faced by listed companies and giving early warning is the direction that companies need to focus on at present (He & Chen, 2020). In the market economy environment, financial risk runs through all the links of capital flows and the financial management of enterprises, which is the comprehensive, concentrated expression of various risks of enterprises and is also a serious problem faced by enterprises (Du et al., 2021). Therefore, to analyze enterprises’ financial risk and detect the occurrence signal of the financial risk in advance can enable the decision-makers of enterprises to control the financial risk in time and take corresponding measures to prevent the occurrence of financial risks and reduce the bad influence on the enterprise.
Recently, the continuous development of science and information technology has brought new opportunities to all walks of life (Huang et al., 2018). The rapid and continuous changes of the market economy have led to a more diverse set of financial risks to enterprises, so traditional risk identification methods are limited (Lin et al., 2018). The continuous promotion of artificial intelligence and the popularization of neural network algorithms have injected new vitality into enterprise development. After the risk indicators for listed companies are simplified by rough set theory, the processed data are input into a back-propagation neural network (BPNN) to calculate listed companies’ financial risk assessment and prediction. The financial risks faced by listed companies are diverse and uncertain (Zhou et al., 2019; Liu et al., 2021). Therefore, they cannot meet the current needs to evaluate them through the existing historical data and experience. Rough set theory provides a powerful foundation for data mining and simplifies the risk data factors and supports selecting key risk indicators (Błaszczyński et al., 2021). Then, the BPNN algorithm trains and learns the risks to establish the financial risk assessment model and improve the scientific and intelligent financial risk early warning. By using scientific principles in the study of financial risk early warning models, listed companies can help enterprises avoid a crisis caused by financial risks and help them develop (Jin et al., 2018; Qiao et al., 2021; Wu et al., 2022; Yu et al., 2021). At the same time, it can also help enterprises make reasonable development plans and project decisions, reduce the adverse effects of market economic turbulence and enhance the ability to resist financial risks.
Based on the existing research, listed companies’ financial risk early-warning models are studied. First, the financial risk characteristics of listed companies are analyzed. Then, the financial risk index factors are simplified through rough set theory, the key indicators are selected, and the financial risk early-warning index system is established. Then, the BPNN algorithm is used to build a financial risk early-warning model to calculate, predict, and evaluate the simplified indicator data. Finally, through empirical analysis, the accuracy and feasibility of the neural network model are verified. At the same time, we briefly analyze the internal and external influencing factors and control methods of financial risk.