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The continuous deepening of the development of my country's market economy has brought more development opportunities to Chinese enterprises, which has made the development of Chinese enterprises more rapid. A financial crisis in an enterprise will bring about disastrous consequences such as investment loss, unemployment of employees, and unrecover...
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... With the development of enterprises and the increase of financial data of enterprises, traditional statistical methods can no longer accurately predict financial status. Some scholars began to use machine learning methods for financial risk prediction and the most widely used are BP neural network [12][13][14], SVM [15,16], and decision tree [17]. As such, Zhou et al. measured and warned the risks of real estate companies through the implementation of the PSO-SVM model [18]. ...
The financial status of an enterprise is related to its healthy and long-term development, and whether the interests of investors and bank loans can be guaranteed. To improve the prediction accuracy of corporate financial risk, this paper proposes a prediction model for corporate financial risk that integrates GRA-TOPSIS and SMOTE-CNN. First, using GRA-TOPSIS to make a comprehensive evaluation of the financial situation of listed companies. Second, the evaluation results are clustered to obtain the scientific level and interval of financial risk, which lays the foundation for the supervised learning of the convolutional neural network. Then, the SMOTE algorithm is introduced to solve the problem of data imbalance of enterprises at all levels, and the focal loss function is used instead of the cross-entropy loss function to further balance the data. Finally, the listed companies in A shares are randomly selected, and experiments were designed to verify the performance of the model built in this paper. The results show that the prediction accuracy of the financial risk prediction model based on GRA-TOPSIS and SMOTE-CNN is 98.57%, which indicates that the model is feasible and has certain reference value.
The rapid expansion of artificial intelligence (AI) technologies presents novel technical solutions to traditional accounting and finance problems. Despite this, scholars in accounting and finance frequently encounter difficulties navigating the extensive and intricate domain knowledge of AI and its continuously evolving literature. To address this gap, this paper conducts a qualitative survey of the implementation of AI methods in accounting and finance. The paper is structured into four sections. Firstly, we examine the conventional accounting and finance issues and their requirement for AI techniques. Secondly, to inform accounting and finance researchers about the potential of AI, we present broad categories of AI applications. Thirdly, we explore recent research on AI solutions to conventional problems. Finally, we highlight emerging trends and possible research directions.
The coronavirus disease (COVID-19) pandemic has caused significant changes in the external environment of enterprises, resulting in tremendous negative impacts. Accordingly, the irregular fluctuation of business data poses a critical challenge to traditional approaches. Therefore, to combat the effects of the COVID-19 pandemic, an effective model is required to proactively predict an enterprise’s performance and simultaneously generate scientific performance optimization solutions. Consequently, at the intersection of artificial intelligence algorithms, operations research, and management science, an intelligent DEA-SVM model, which has a theoretical contribution, is developed in this study. The capabilities of this model are verified through sufficient numerical experiments. On the one hand, this model outperforms traditional algorithms in prediction accuracy. On the other hand, effective performance optimization solutions for low-performance enterprises are obtained from the input–output perspective. Moreover, the application value of this model is reflected in its successful implementation in the healthcare industry. Thus, it is a user-friendly tool for realizing the stable operation of enterprises in the context of the COVID-19 pandemic.
The rapid development of the big data era has brought great opportunities and challenges to the financial management of enterprises. Starting from the relationship between financial risk and transaction risk in the big data environment, this paper analyzes the problems existing in the construction of financial risk early warning mechanism, and puts forward corresponding countermeasures.KeywordsFinancial riskBig dataEarly warning mechanism
