Enterprise Financial Management Research Articles

Data Analysis and Optimization Strategies in Financial Management

In the increasingly fierce market competition, the decision-making level of enterprises can directly affect their future. Once a decision is made incorrectly, it can cause a significant crisis for the company. The information data in enterprise financial management is the most important reference for company leaders when making decisions. In corporate governance, financial analysis can comprehensively reflect the operational situation of a company. Financial analysis can provide strong data support for the production and operation of enterprises, and it is an effective way to improve operational efficiency. This article optimizes asset allocation based on data analysis and optimization strategies to achieve maximum return on investment portfolio. The current value, annual return, and risk level of stocks for different asset classes are 10 million yuan, with an annual return of 15%. The current value of the bond is 8 million yuan, with an annual yield of 6%. This article provides a reliable basis for financial management and business decision-making.

Innovative Strategies for Financial Investment Management Strategies of Enterprises in the New Era

With the in-depth development of economic globalization and the continuous emergence of new technologies, the importance of enterprise financial investment management strategy is becoming more and more prominent. The traditional financial investment management strategy can no longer meet the needs of enterprises in the new era and needs to be innovated with the times. For enterprises, the effectiveness and scientificity of financial investment management strategy will directly affect the competitiveness and long-term development of enterprises. Therefore, this study chooses the innovation based on the financial investment management strategy of enterprises in the new era as the research theme, aiming at exploring the innovative strategy to adapt to the needs of enterprises in the new era.

Financial big data management and intelligence based on computer intelligent algorithm

With the acceleration of China’s economic integration process, enterprises have gained greater advantages in the fierce market competition, and gradually formed the trend of grouping and large-scale. However, as the scale of the company increases, the establishment of a branch also causes many problems. For example, in order to obtain more benefits, the business performance of the company can generate false growth, resulting in financial and operational risks. This paper analyzed the current situation and needs of enterprise financial control from two aspects of theory and practice, combined with specific engineering projects, taking ZH Group as an example, according to the actual situation of the enterprise. The article first introduces the basic situation of the enterprise; Then, the financial control strategy was designed, and different modules were designed to achieve financial control; Afterwards, use a reverse neural network to evaluate the effectiveness of financial management and risk warning; Relying on particle swarm optimization algorithm to seek the optimal solution and applying it to financial management and risk warning, in order to improve the level of introspection and risk management in decision-making. Finally, the value of computer intelligence algorithms in financial big data management is evaluated by constructing a financial risk indicator system. Through the analysis of enterprise financial management, the total asset turnover rate of ZH Group decreased by 0.39 times in 5 years. After 5 years of adjustment of the company’s business, the company’s overall operational capabilities still needed to be improved, and the company’s comprehensive business capabilities also still needed to be improved. Therefore, the application of intelligent algorithms for financial control is very necessary.

Construction of Enterprise Financial Compliance Management System in the Context of Digital Taxation

In the context of digital taxation, constructing an enterprise financial compliance management system is an important means to ensure compliance with tax regulations, optimize tax management, and improve economic efficiency. As a crucial component of modern tax administration, "digital taxation" imposes new requirements on enterprise financial compliance management. This paper aims to explore how to construct an effective enterprise financial compliance management system under the backdrop of "digital taxation" to enhance the quality of accounting information and financial compliance, thereby reducing financial and tax risks and promoting the sustainable development of enterprises.

DEVELOPMENT OF THE VALUE BASE MANAGEMENT CONCEPT AND ITS ROLE IN MAXIMIZING THE VALUE OF THE CORPORATE ENTERPRISE

The purpose of the study is to critically review and summarize the main world approaches to building Value Base Management and to substantiate, on this basis, recommendations for its implementation in the domestic practice of financial management. The article describes the evolution of VBM development and conceptual approaches to corporate enterprise management. The objective necessity of transforming approaches to the management of corporate enterprises from traditional financial models to value-based management is substantiated. It is established that VBM is not only one of the main directions of improving the efficiency of management decisions, but also has a significant impact on the value of a corporate enterprise in the long term. The article proves that the use of the value-based concept of enterprise financial management implies the selection of appropriate methods and tools at all levels of management aimed at sustainable value reproduction. 
 The article considers the process of transformation of the VBM concept and its main elements. A comprehensive analysis of the main approaches and models to business valuation is presented. A comparative characterization of VBM and the traditional approach to corporate enterprise management is systematized and provided. 
 The generalization of the economic content of management theories has allowed to substantiate that VBM differs significantly from the previously accepted management criteria and is a more effective concept of equity management than other systems. It should be borne in mind that the value of an enterprise, on the one hand, is an indicator of its sustainable development, and on the other hand, it is a regulator of value distribution for various participants in economic relations, so, of course, the management mechanism should focus on the key factors of value creation

Design and Implementation of Management Information System for University Library

With the rapid development of the Internet, information processing work in universities has also increased, from libraries to electronic book management platforms, everything is available. Although the number of libraries we have is constantly increasing and people's attention to them is also increasing, their existence is often universal, non-existent, and sometimes incomplete, which brings great difficulties to our daily management. Analyzed the workflow and functional requirements of university libraries, and applied them in practice to achieve better results. On this basis, the paper provides a detailed analysis of the main functions and various forms of the enterprise financial management information system. SQL Server 2008 is a database based system with high confidentiality and large-scale storage capacity. Through this concept and approach, one can better grasp and grasp the business skills required in daily work and work, greatly improving work efficiency. A library MIS based on AIS/S model was constructed using an A/S model and a SQL Server database. This software is user centered and librarian centered. They proposed a system to the library that maximized its workflow and improved its service management and supply.

Architecting an enterprise financial management model: leveraging multi-head attention mechanism-transformer for user information transformation

Financial management assumes a pivotal role as a fundamental information system contributing to enterprise development. Nonetheless, prevalent methodologies frequently encounter challenges in proficiently overseeing diverse information streams inherent to financial management. This study introduces an innovative paradigm for enterprise financial management centered on the transformation of user information signals. In its initial phases, the methodology augments the Transformer network and self-attention mechanism to extract features pertaining to both users and financial data, fostering a more cohesive integration of financial and user information. Subsequently, a reinforcement learning-based alignment method is implemented to reconcile disparities between financial and user information, thereby enhancing semantic alignment. Ultimately, a signal conversion technique employing generative adversarial networks is deployed to harness user information, elevating financial management efficacy and, consequently, optimizing overall financial operations. The empirical validation of this approach, achieving an impressive mAP score of 81.9%, not only outperforms existing methodologies but also underscores the tangible impact and enhanced execution prowess that this paradigm brings to financial management systems. As such, this work not only contributes to the state of the art but also holds promise for revolutionizing the landscape of enterprise financial management.

Application of voice enhancement based on virtual sensor network in enterprise financial management

Based on virtual sensor network, this paper explores the application of voice enhancement technology in enterprise financial management. Virtual sensor network is a low cost, easy to expand and flexible network system based on simulation and simulation technology. In this network system, the virtual voice sensor node can collect and process the voice information inside the enterprise. Speech enhancement technology uses signal processing and machine learning algorithms to reduce noise, enhance and clarify speech signals, thereby improving the accuracy and reliability of speech data. By constructing virtual voice sensor nodes in virtual sensor networks and applying voice enhancement technology, the research results show that enterprises can collect and process voice data more accurately in the process of financial management. Voice enhancement technology can reduce the impact of environmental noise on financial data and improve the clarity of voice signals, so as to effectively improve the quality of data. Through the voice enhancement technology based on virtual sensor network, high-quality voice data acquisition and processing can be realized in the financial management of enterprises, improve the accuracy and effect of financial decision-making, and reduce the cost and complexity of enterprises.

The impact of the enterprise financial risk management function on financial performance in Bosnia and Herzegovina

Abstract Adequate enterprise financial risk management (EFRM) represents a leading competitive advantage of enterprises that determines market survival and business success in an uncertain global environment. Over time, EFRM has become a constituent part of integral business dealings of enterprises and one of the strategic functions of enterprise management. The main purpose of the paper is to explore the effects of the EFRM function/system on the financial performance of enterprises in Bosnia and Herzegovina (BiH). The basic source of data in the research was collected by means of a structured questionnaire. The target population in the research consists of large enterprises that have continuously operated in the territory of BiH (2013-2017). The selection of enterprises was made applying a random sampling method and contains 72 enterprises. Appropriate descriptive and inferential statistical methods were used in the data analysis and panel data analysis was used to assess effects of EFRM function on financial performance. The scientific contribution of the paper is reflected in the fact that the research is pioneering for Bosnia and Herzegovina with the analysis of effects of the EFRM function on enterprise financial performance (EFP). The results show that there are no systematic, statistically significant differences between large enterprises that engage in risk management (‘hedgers’) and enterprises that do not engage in risk management (‘non-hedgers’) in BiH.

Mode Innovation and Discussion of Enterprise Financial Management in the New Economic Background

The development of the globalized economy has also made enterprise financial management work into a new stage of reform, the; transformation of the market economic system has also made the enterprise financial management work content and mode of change the traditional financial management model has failed to meet the needs of the current enterprise, and even hinder the development of enterprises. In the new economic background promoting financial management reform and innovation is not only the way of enterprise development. It is also in line with the trend of the times, where the article through the trend of economic development and in-depth analysis of the current financial management situation.By analyzing published literature and using the literature analysis method, we summarize the current problems in corporate financial management and propose corresponding solutions to the problems based on the key points of the problems, thereby providing reference and help for corporate development.

Blockchain technology concerns and corporate financial risk prevention—A quasi-natural experiment for Chinese listed A-share companies

The rapid development of blockchain technology (BT) has gained widespread attention from all sectors of society. In order to remain competitive, enterprises have responded positively to this research and development, which is conducive to the enhancement of their own digital construction. BT can provide technical support for companies to strengthen their internal management and reduce business risks caused by information asymmetry. However, the ability to reduce enterprise financial risk effectively depends on the breadth and depth of BT concerns. This study constructs word-frequency statistical variables of BT concerns based on the annual report data of listed companies. It conducts an empirical study on the issue of BT concerns affecting corporate financial risk prevention and regulation based on a propensity score matching difference-in-difference model. The results show that: (1) Attention to BT significantly reduces corporate financial risk (2) In terms of the mechanism effect, Attention to BT can significantly reduce corporate financial risk by optimizing accounts receivable management, enhancing debt servicing ability, and improving corporate performance. (3) The heterogeneity analysis shows that the concern of BT in reducing financial risk is even more significant for enterprises whose social responsibility is not effectively regulated and for hybrid enterprises. This study enriches the literature on the mechanism of BT to enterprise financial risk and provides a reference for both enterprise-level blockchain concerns and strengthening enterprise financial risk management.

Research on the Optimization of Enterprise Financial Management under the Financial Sharing Model

With the advancement of globalization and information technology, the financial sharing mode has gradually emerged as a crucial means for enterprises to optimize their financial management. Particularly within the context of economic globalization, informatization, and digital transformation, enterprises find themselves navigating a rapidly evolving market environment by intensifying competition. To enhance efficiency and competitiveness, many enterprises have embraced the financial sharing model to streamline financial management processes, curtail costs, and bolster the execution of corporate strategies. This article aims to dissect the definition and essence of the financial sharing model and its significance in the realm of enterprise financial management. Drawing upon this analysis and aligning with the needs of enterprise financial management, the article proposes ideas for optimizing management practices, aspiring to foster reform and innovation in enterprise financial management while enhancing its level of financial management and ability to respond to financial risks. This contribution seeks to provide valuable insights for practitioners in the field.

An advanced approach to operational quality evaluation for industry-finance integration enterprises based on integrated interval-valued intuitionistic fuzzy multi-attribute decision making

With the acceleration of economic globalization, enterprises are facing fierce competition and huge challenges, requiring deep financial management transformation. In this context, the integration of industry and finance has gradually demonstrated extremely important value. The integration of industry and finance can not only effectively improve the efficiency of financial management, prevent business risks, and improve operational efficiency, but also enhance the comprehensive ability of enterprise financial management, providing a more flexible, transparent, and efficient financial management system for enterprises. The operational quality evaluation of industry-finance integration enterprises under lean management accounting is a multiple-attribute decision-making (MADM). In this paper, some calculating laws on IVIFSs, Hamacher sum, Hamacher product are introduced, and the interval-valued intuitionistic fuzzy Hamacher interactive power averaging (IVIFHIPA) technique is proposed based on the interval-valued intuitionistic fuzzy (IVIF) Hamacher interactive weighted averaging (IVIFHIWA) technique and power average (PA) technique. Meanwhile, some ideal properties of IVIFHIPA technique are studied. Then, the IVIFHIPA technique is employed to cope with the MADM under IVIFSs. Finally, an example for operational quality evaluation of industry-finance integration enterprises under lean management accounting is employed to test the IVIFHIPA technique. Thus, the main research aim of this paper is concluded as follows: (1) the IVIFHIPA technique is constructed based on IVIFHIWA technique and classical power average (PA) technique; (2) the IVIFHIPA technique is put forward to cope with the MADM under IVIFSs; (3) an empirical example for operational quality evaluation of industry-finance integration enterprises under lean management accounting has been put forward to show the IVIFHIPA technique.

A Study of the Impact of Digital Applications on the Financial Management of Modern Enterprises

With the rapid development of science and technology, China has begun to step into the era of digital economy. The arrival of the digital economy for the development of China’s enterprises has brought great impact. In enterprise management, financial management is the management of the basis of the guest; more needs to change with the times and changes. However, at present, most of China’s enterprise’s financial management mode is relatively traditional. Unable to meet the needs of the era of textbook economy. This requires enterprises to address the characteristics of the digital era and accelerate the construction of enterprise financial management innovation. In this paper, This article will use the literature comparison method to analyze in detail the problems and situations currently encountered in financial management under the digital economy, and put forward some reasonable suggestions for the transformation of corporate financial management models under the background of the digital economy.

Intelligent Optimization Model of Enterprise Financial Account Receivable Management

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.

Design and Implementation of Data Management and Visualisation Module in Financial Digital Management

Enterprise financial data is the key indicator of enterprise development, which provides the important basis for management to analyse and make decisions. Therefore, the provision of reliable and effective information services to enterprises through visualisation technology has become an urgent problem to be solved in the construction of enterprise informatisation. At present, the common data statistics and visualisation tools in the market are difficult to meet the needs of specialised financial enterprises for data analysis. Additionally, the current financial management system has several issues, including an abundance of data and lack of observation suitability. Aiming at the deficiency of data management function in the system, this paper studies the improvement design of data management and visualisation module in financial digital management. First, [Formula: see text]-means clustering algorithm and C4.5 decision tree algorithm are selected to improve the financial data management system. Then, through the existing hierarchical data visualisation scheme, the node link method, space filling method and Sankey chart are proposed to display the changes of financial data. Finally, the data management and visualisation module and the corresponding algorithm flow are designed. The experiment indicates a contour coefficient of 0.53 for the performance evaluation model based on the [Formula: see text]-means algorithm, indicating a satisfactory clustering result. The employee violation prediction model, based on the C4.5 decision tree algorithm, exhibits a high prediction accuracy of 92.35% for the training dataset, demonstrating its effectiveness in predicting employee violations. The data rendering accuracy of the visualised tool is 98.46%, significantly surpassing that of traditional visualisation tools. At the same time, its visual effect and operation are better than traditional tools. Compared with the traditional data visualisation system, this research method improves the efficiency of enterprise financial data management, converts complicated financial data into graphics that are easier for people to understand, realises visualisation, and effectively reduces the management cost of financial operations.

An integrated methodology for enterprise financial management capability evaluation based on EDAS technique and group decision making

The financial management capability of enterprises, as an important component of their soft power, has a decisive impact on the success or failure of their operations. In the increasingly fierce market competition, enterprises must continuously improve their financial management capabilities in order to ensure efficient operation and achieve better economic benefits. Insufficient financial management capabilities in enterprises can seriously affect the stability of production and operation, hinder the realization of profits, and hinder the long-term development of enterprises. In order to better improve the financial management level of enterprises and promote the standardization of financial management, it is necessary to use scientific techniques to evaluate the financial management ability of enterprises, so as to accurately grasp the key links in the financial management process of enterprises and implement targeted effective measures. The enterprise financial management capability evaluation is a classical multiple attribute group decision making (MAGDM). In recent years, the MAGDM problem has become an important research field in modern decision science. This paper extends the EDAS technique to the 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). On the basis of the original EDAS technique, 2-tuple linguistic Pythagorean fuzzy number EDAS (2TLPFN-EDAS) technique based on cosine similarity measure (CSM) and Hamming distances is managed for MAGDM. Finally, a case study for enterprise financial management capability evaluation and some comparative analysis with the other techniques show that the new technique proposed in this paper is effective, reasonable and accurate. The main contribution of the paper is summarized: (1) the 2TLPFN-EDAS technique based on CSM and Hamming distances is managed for MAGDM under 2TLPFSs; (2) The entropy is employed to manage the attribute weight based on cosine similarity measure(CSM) and Hamming distances under 2TLPFSs; (3) the 2TLPFN-EDAS technique is employed for enterprise financial management capability evaluation and were compared with some existing techniques; (4) Through the comparison, it is found that 2TLPFN-EDAS technique for enterprise financial management capability evaluation proposed are effective.

Retracted: Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network.

[This retracts the article DOI: 10.1155/2022/7728596.].

Research on the Application of Data Mining Techniques in Early Warning Models for Financial Management

Abstract In the process of accelerating enterprise development in China, enterprises are prone to financial management crises while grasping development opportunities. This paper is oriented towards enterprise financial management data and proposes an adaptive neuro-fuzzy inference system to reason about risky data. The ANFIS model is integrated into K-mean clustering to provide early warning of enterprise risk by dividing different financial management risk indicators. The validation method uses both the comparison of similar algorithms and the practical test of A enterprise. Compared to other analysis models, the accuracy of the early warning model constructed by the K-Means algorithm is as high as 99.64%, which is suitable for enterprise financial management early warning. Using the model built in this paper to cluster the types of enterprise financial risk, the second cluster centers clusters of enterprises in financial difficulties, a number of indicators show negative results, and the net sales interest rate is as low as −94.23. This indicator is applied to the financial crisis of Company A’s financial situation analysis and found to be consistent with the characteristics of the second cluster center. This verifies the accuracy of the financial early warning model constructed in this paper.

Analysis of enterprise financial management under the background of digital transformation

Along with the continuous development of our economy and the continuous improvement of the level of social information, the era of big data has already come quietly, and the digitalization is playing a more and more crucial role in the financial management of enterprises in our country. In the past, people’s understanding of digitalization might only be to do some data analysis and data management work, which led to a series of problems, for instance, organizational management is blunt and not flexible enough. These problems caused the slow development of enterprises, and the productivity of enterprises was difficult to achieve further liberation. Under the background of digital transformation, the traditional financial management of enterprises can no longer satisfy the effective daily operation and management of enterprises. If enterprises are desperate to develop, finance has to be transformed. Therefore, Chinese enterprises ought to progress in the direction of informatization and digitalization. Hence, this paper intends to use literature research and other methods to make up for the shortcomings of traditional enterprise financial management in this study, expand the content of digital transformation of enterprise financial management, and promote the implementation of digital strategy in order to better promote the solution of related problems.