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With the need of social and economic development, the audit method is also continuously reformed and improved. Traditional audit methods have defects of comprehensively considering various risk factors, and cannot meet the needs of enterprise financial work. To improve the effectiveness of audit work and meet the financial needs of enterprises, a solution for intelligent auditing of enterprise finance is proposed, including intelligent analysis of accounting vouchers and of audit reports. Then, Bi-directional Long Short-Term Memory (BiLSTM) neural network is used to classify the audit problems under three text feature extraction methods. The test results show that the accuracy, recall rate, and F1 value of the COWORDS-IOM algorithm in the aggregate clustering of accounting vouchers are 85.12, 83.28, and 84.85%, respectively, which are better than the self-organizing map algorithm before the improvement. The accuracy rate, recall rate, and F1 value of Word2vec TF-IDF LDA-BiLSTM model for intelligent analysis of audit reports are 87.43, 87.88, and 87.66%, respectively. This shows that the proposed method has good performance in accounting voucher clustering and intelligent analysis of audit reports, which can provide guidance for the development of enterprise financial intelligence software to a certain extent.

In the era of big data, data information has exploded, and all walks of life are impacted by big data. The arrival of big data provides the possibility for the realization of intelligent financial analysis of enterprises. At present, most enterprises’ financial analysis and decision-making based on the analysis results are mainly based on human resources, with poor automation and obvious problems in efficiency and error. In order to help the senior management of enterprises to conduct scientific and effective management, the study uses big data web crawler technology and ETL technology to process data and build an intelligent financial decision support system integrating big data together with Internet plus platform. J Group in S Province is taken as an example to study the effect before and after the application of intelligent financial decision support system. The results show that the crawler technology can monitor the basic data and the big data in the industry in real time, and improve the accuracy of decision-making. Through the intelligent financial decision support system which integrates big data, the core indexes such as profit, net asset return, and accounts receivable of the enterprises can be clearly displayed. The system can query the causes of financial changes hidden behind the financial data. Through the intelligent financial decision support system, it is found that the asset liability ratio, current assets growth rate, operating income growth rate, and financial expenses of J Group are 55.27, 10.38, 20.28%, and 1,974 million RMB, respectively. The growth rate of real sales income of J Group is 0.63%, which is 31.27% less than the excellent value of the industry 31.90%. After adopting the intelligent financial decision support system, the monthly financial statements of the enterprises increase significantly, and the monthly report analysis time decreases. The maximum number of financial statements received by the Group per month is 332, and the processing time at this time is only 2 h. According to the results, it can be seen that the intelligent financial decision support system integrating big data as the research result can effectively improve the financial management level of enterprises, improve the usefulness of financial decision-making, and make practical contributions to the field of corporate financial decision-making.

With the deepening of enterprise financialization, the trend of "moving away from reality to emptiness" has increased the difficulty of financial management in the manufacturing industry. This paper selects the data of A-share main board listed companies from 2012 to 2021 to study the motivation of financial investment in the manufacturing industry and its impact on financial risk. The research results show that the main motivation of listed companies' financial investment in the manufacturing industry is "substitution" motivation. With the purpose of maximizing profits, the excessive allocation of monetary assets, especially long-term financial assets, increases financial risks of enterprises. Furthermore, the financial risk caused by the financial investment of state-owned enterprises is greater. [ABSTRACT FROM AUTHOR]

This study investigates the impact of economic policy uncertainty (EPU) and stock market development (SMD) on natural resources in the United States during the period between 1985 and 2021 by keeping the GDP and innovation as control variables. For empirical analysis, the autoregressive distributed lag model (ARDL) and Kernel Regularized Least Squares (KRLS) regression techniques were used to investigate the long‐ and short‐run relationships. To check the robustness of results, this study uses a machine learning approach, i.e., Canonical Cointegrating Regression (CCR). The results of the ARDL bounds test approach suggest the presence of long‐run relationships among the study variables, and these results were confirmed through KRLS and CCR. The long‐run results indicate that economic policy uncertainty and SMD increase natural resource rent. Moreover, GDP and innovation also increase natural resource rent. However, a weak relationship exists between innovation and natural resource rent in the short run. Policymakers need to provide stable and encouraging economic policies to promote sustainable resource management, while stock markets need to facilitate long‐term planning and responsible investment in natural resource industries. [ABSTRACT FROM AUTHOR]

As a key component of enterprise assets, accounts receivable play an important role in enterprise financial management and determine the long-term development of enterprises in the later period. In order to minimize the financial risk brought by the credit sales of enterprises, this subject studies the intelligent optimization of enterprise financial account receivable management. BP neural network and K -means clustering algorithm are used to evaluate the risk of account receivable and the owner's credit, respectively. The account balance accounts for 45.20% of the total amount, and the risk rating of accounts receivable is 4. The training result of BP neural network algorithm has high accuracy. With K -means clustering algorithm, accurate evaluation of owner's credit can be achieved, which can provide reference for optimization of enterprise account receivable management mode. [ABSTRACT FROM AUTHOR]

Under the influence of environment, enterprise index and other factors, the result of enterprise financial development path analysis is not ideal, so the development path of green enterprise financial development in coastal cities is studied. The regional differences of the green economy of coastal cities are analyzed. Under the premise of considering the differences, the competitiveness of the financial development of green enterprises in coastal cities is clarified, and the constraints that affect the financial development path are obtained. According to the development goal of enterprise financial integration, all the influencing indexes that affect the development path of enterprise finance are obtained, and the concrete path of enterprise financial development is finally gotten. A port city is selected as the experimental object to verify, under the condition of low impact degree and high change of index, using expert evaluation method to compare the design method and existing methods. According to the results, the average expert evaluation score of the proposed method is 87.8, higher than that of the control method. The results show that the proposed path analysis method is more suitable for the analysis of the current situation of green enterprise financial development, and has more practical significance. [ABSTRACT FROM AUTHOR]

In view of the poor effect of traditional management methods, a quantitative model of financial risk management for forestry enterprises based on nonlinear differential equation is proposed. According to the definition of forestry enterprises, this paper analyzes the development status of forestry enterprises from the quantitative analysis and industrial analysis of forestry enterprises. This paper constructs a nonlinear differential equation model, and analyzes the risks faced by enterprises in operation. The specific application content is studied, and the quantitative model analysis of financial risk management of forestry enterprises is completed. The simulation results show that the management effect of this method is good, which lays a foundation for the protection of forestry economy. [ABSTRACT FROM AUTHOR]

Aiming at the real user – commodity behavior data of Alibaba mobile e-commerce platform, the big data development of mobile e-commerce is analyzed in this paper, and a recommendation algorithm based on the mobile e-commerce deep learning is put forward. The study of the recommendation algorithms applies to the information age with the rapid growth of data. The results have verified that the recommendation algorithm proposed in this paper can extract useful information features from extensive information and recommend to the users who need such information to meet the information needs of different users, save the time required for users to search for the information, and improve the utilization of the information. These results indicate that the mobile e-commerce recommendation algorithm based on the cone depth learning has good practicality. It is concluded in this paper that the recommendation algorithm has a relatively high recommendation effect in the mobile e-commerce scenario and that its ideas and results have essential value for the study of mobile e-commerce. [ABSTRACT FROM AUTHOR]