Enterprise Financial Management and Decision System Based on Improved Decision Tree

The majority of traditional finance management in colleges and universities is manual. This backward management mode brings a lot of inconvenience to financial processing. An essential concern in the daily financial administration of colleges and universities is how to efficiently gather, handle, and evaluate this important financial information and apply this beneficial knowledge to the daily management of colleges and universities. College and university finance administration has become increasingly complex due to the rapid growth of these institutions. The standard financial accounting management system is far from adequate for the daily needs of colleges and universities as their size grows. As a result, a new financial accounting management system for universities is being developed based on the combination of big data and blockchain data. The embedded processor, DDR2 memory chip, network interface, and USB interface are all designed by the hardware section. The software element examines the needs of university financial accounting management before designing a model based on big data and blockchain integration for university financial accounting management. Finally, the financial accounting management function module and database are designed and tested. The experimental results show that the designed financial accounting management system can effectively carry out financial management, has good performance, and has certain application value.

With the deepening of the reform of higher education management system in China, the financial management model of colleges and universities appears to be of essence. Financial management is the core of economic management in colleges and universities. With the development of market economy, the operating subject and mode of colleges and universities have changed, the economic interests of colleges and universities have become more diversified, the economic relations have become more complicated, and the financial management has become increasingly detailed, requiring colleges and universities to establish a suitable, reasonable and effective financial management system to straighten out the financial relations and promote the sustainable and healthy development of colleges and universities. At present, management accounting has widely used in the financial management of colleges and universities, making the financial management of colleges and universities more efficient and scientific and promote the healthy, coordinated and sustainable development of colleges and universities.

In the current context of rapid development of digital economy, enterprise financial management is facing increasingly complex internal and external environments, and the traditional financial management model has been difficult to meet the needs of intelligence, refinement and efficiency. This paper analyzes the status quo and development trend of enterprise financial informatization construction, and clarifies the important role of informatization in improving the efficiency of financial management; from the three dimensions of data-driven decision-making support, financial process optimization based on artificial intelligence, and the deep integration of informatization and intelligence, it discusses the key elements of the optimization of the intelligent financial management system and the path of achieving it; finally, it evaluates the effect of optimized intelligent financial management system through a typical case, and verifies that it can meet the demand of intelligent financial management and improve its efficiency. Finally, the optimized intelligent financial management system is evaluated through typical cases to verify its significant role in improving management efficiency, reducing operating costs and enhancing decision-making science. The study shows that promoting the organic combination of informationization construction and intelligent management can not only improve the overall level of enterprise financial management, but also provide an important guarantee for enterprises to achieve sustainable development.

The article addresses the relevant scientific and practical issue of developing innovative approaches to forecasting in enterprise financial management in the context of economic digital transformation. The necessity of using intelligent forecasting tools based on modern technologies of artificial intelligence, machine learning, big data analytics, and neural networks is substantiated. The main advantages of implementing such tools are identified, including increased accuracy and speed of financial analysis, enhanced adaptability of strategic planning, and reduced subjectivity in decision-making processes. The study systematizes key models of intelligent forecasting: ARIMA, GARCH, LSTM, CNN, decision trees, clustering algorithms, hybrid approaches, and fuzzy logic. Practical examples of their application in financial management are provided, including cash flow forecasting, investment attractiveness evaluation, risk analysis, enterprise development scenario modeling, and fraud detection. It is shown that intelligent technologies play a leading role in building flexible and resilient financial management systems capable of self-learning, self-regulation, and dynamic restructuring under uncertainty. At the same time, the main limitations of implementing intelligent systems in enterprise financial operations are outlined: high requirements for the quality and structure of input data, complexity of interpreting forecast results, shortage of qualified personnel, and the need for significant computational resources. The study emphasizes the importance of developing appropriate digital infrastructure, organizational transformation, and interdisciplinary cooperation for the successful integration of such tools. International experience in the application of intelligent forecasting by leading companies in digital transformation is generalized. A classification of major categories of forecasting tools by functional purpose is proposed, and practical recommendations for their adaptation to the Ukrainian context are developed. The results obtained can be used as an analytical and methodological basis for modernizing enterprise financial management systems under the new economic reality.

With the rapid development of information technology and network, the modern enterprise management model is also undergoing tremendous changes, companies must adapt to the financial management of network and information technology development.Enterprise financial management system is a financial planning, control, supervise and manage the use of information systems to ensure that the company's good operating and financial standardized management, data analysis and processing it, improve financial management, and provide strong support for enterprise information management leadership and decision-making.

The sudden arrival of the new crown epidemic has had a significant and long-lasting impact on the division's economic environment as well as the production and operation activities of businesses. As far as the financial management is concerned, opportunities and difficulties are faced by enterprises of all types. With reference to the available research data, enterprises have an important contribution to GDP and jobs, but they still face a series of difficulties and challenges in their development in the context of the normalization of the epidemic. By analyzing the impact of the new crown pneumonia epidemic on the financial management work of enterprises, this paper proposes an artificial neural network-based enterprise financial forecasting and early warning method to provide an effective method for enterprise financial management. For the time-series characteristics of enterprise finances, a prediction model based on long- and short-term memory networks is developed which acknowledges the necessity of combining the temporal dimension with the spatial dimension for forecasting. This model incorporates time qualities into the data to the existing forecasting model. It also considers both working and nonworking day data and thoroughly considers the factors influencing corporate finance. Then, using BP neural network for financial risk prediction, nonfinancial index factors should be added to the financial early warning model thus eliminating the limitations of the financial early warning model. At the same time, the accuracy of the prediction can be improved which is more suitable for enterprises to apply in practice. The experimental results demonstrate that the financial prediction model built by multilayer feed forward neural networks and recurrent neural networks based on error back propagation training is inferior to the prediction model built by long- and short-term memory network. Regardless of the degree of fitting or prediction accuracy, the BP neural network model outperforms the conventional model for enterprise financial warning. Under the normalization of the pandemic, the combined use of both can offer an efficient technique for enterprise management.

Financial management, as the central link in the enterprise management chain, has emerged as a critical component in achieving the company’s strategic objectives. Businesses are transitioning from a closed to an open and scalable development model to an intensive development model, which is reflected in financial management and necessitates a growth-oriented transformation. Unreliability abounds in today’s globalized environment, from financial management-oriented accounting to efficiency accounting. In addition to general supply and demand unreliability, the high degree of complexity and increased channel unreliability make companies resist the great challenge of financial management systems’ ability to face uncertain events, corporate supply chains become increasingly vulnerable, and unreliability has become a common and indispensable phenomenon in many economic and management decision problems. Due to their high data requirements and dynamic adaptability, Markov models are used as the primary forecasting tool. They do not require a large amount of historical data, but rather a detailed analysis of the recent past. This thesis examines and resolves the issues with the current financial management system through a detailed examination of the enterprise financial management system’s reliability optimization, as well as identifying and supplying the appropriate financial system management tools for the company’s business activities. New business concepts and their advantages to management are introduced. The experimental results show that adding precision constraints to the floating-point calculation problem transforms it into an integer calculation problem, improving reliability by 0.389, demonstrating that the Markov model improves the efficiency of financial resource allocation. At the decision-making level, it provides a management platform and operational tools. As a result, research on this topic will assist businesses in developing a comprehensive business process that meets their needs and adapts to future development needs at a low cost, as well as assisting businesses in communicating with employees, external collaboration, and collaborative business between customers and partners.

China’s higher education industry has developed rapidly since the Third Plenary Session of the Eleventh Central Committee. With the acceleration of the development of institutions of higher learning, the problems and contradictions in its financial management have become increasingly prominent. More and more complicated. The purpose of this article is to optimize the structure and technology of existing financial business processes and financial management models, bring big data technologies that are suitable for their own development to financial business management, and integrate them with financial management theories and institutions of higher education. Innovate and improve the new management mode to meet the needs of financial business and management control, so as to achieve a comprehensive improvement of the financial management level of colleges and universities (CU). This paper proposes a research on the subject of financial management mode of CU based on big data technology, discusses the problems existing in actual work, and elaborates on the problems from the design of accounting business processes, internal control systems, and construction of financial information platforms, and proposes corresponding Improve opinions and strengthen the actual basis of this article. By studying the development history of China’s college financial management (FM) model and analyzing the existing college FM model, it is found that there are big management situations such as weak management concepts, imperfect budget management, and the ability of financial staff to adapt to university reforms. The need to reconstruct the traditional university FM process and other issues, put forward the efficiency of the new model of university FM under big data.KeywordsBig data technologyCollege financeFM modelInnovation and improvement

With the gradual deepening of the reform of the higher education management systems in China, the focus of the financial work in colleges and universities has also changed from accounting to the management efficiency. Under such a situation, the important role of the financial analysis in the financial management and decision-making in colleges and universities is self-evident. Through the construction of the computer financial management system, managers can make more efficient use of the internal and the external information, improve the levels and efficiency of the financial management, and then better improve the economic efficiency.

With the maturity and popularization of cloud computing technology, the informationization of enterprise financial management system becomes the development trend. In this context, this article researches and designs enterprise financial management system based on cloud platform. The overall model adopts MVC pattern to develop and use Struts + Spring + lbatis and build it under the B/S framework of J2EE. The system safety management is realized by multi-functional module division. With the help of this system, it can effectively promote the smooth flow of enterprise financial information and realize fine management, which has theoretical and practical significance for the transformation of enterprise financial management to informationization, digitalization and intelligentization.

With the development of the current era, China’s Internet and computer technologies are constantly updated and improved. Under the application of information technology, great changes have taken place in the financial management work of schools. At present, the content of school financial management work is constantly increasing. In order to meet the needs of school management, it is necessary to improve the level of financial management, strengthen the informatization construction of financial management, so as to realize the modernization of school financial management and improve the efficiency of financial management. Big data technology and information technology can realize the rapid collection and preprocessing of information, achieve in-depth mining of data value, assist staff in quickly identifying problems in their work, and further improve work efficiency. Based on this, this paper conducts in-depth analysis and research on the informatization construction of financial management in colleges and universities on the basis of big data, for reference.

The methodological and practical recommendations for applying a conceptual approach to forming an effective mechanism for managing financial security at business enterprises under conditions of economic recovery are substantiated. Despite the existence of some proposals for forming effective mechanisms for managing financial security at the level of market participants, the practice of implementing and arranging their components by stages and distribution by functional performers is a matter of active discussion in academic circles. The ontological content of the scientific category “financial security of enterprise” at the macro and micro levels is generalized. The author focuses on the need to consider the enterprise's financial security as an integral management system with an internal structure, elemental composition and feedback components. The components of the enterprise financial security management system are specified. The author's own interpretation of the essence of the concept of “enterprise's financial security management mechanism” as a set of tools and levers of influence on managerial decision-making in the field of ensuring an appropriate level of protection of vital financial interests of a market entity from challenges and threats of the external environment is proposed. The stages of forming a financial security mechanism at business enterprises are recommended, including: analysis of the enterprise's current status, development of a strategy to overcome the identified risks, implementing measures, monitoring and evaluating the implemented measures. At the conceptual level, the author's version of the mechanism for managing financial security of the business enterprise with the appropriate support tools (financial instruments, financial levers, financial methods and indicators, information and analytical subsystem) and four key components (organizational, methodological, technological and financial) is proposed. Promising directions for improving the organizational support of the financial security management mechanism at business enterprises with the assignment of responsible managers and specification of the timeframe for implementing measures are identified. The expediency of distributing responsibility among the specialists for the financial subsystem at business enterprises is substantiated.

Financial aspect of planning is analysed as the main part system of the general planning of the activity of an enterprise. The environmental instability and the uncertainty of economic conditions influence the activity of the enterprise because the effectiveness of the activity of the company largely depends on the state of financial planning at the enterprise. The higher level of uncertainty the more important becomes planning. Its correctness and accuracy of the forecast influence the success or failure of business activity. Financial planning as a key part of the whole planning process makes it possible to implement the strategic plans of the company through the development of specific action plans for the planning period. It allows calculating the efficiency of available resources, economic and financial results. The article analyses the current system of financial planning and management in domestic enterprises automotive industry on the example of group of enterprises of Corporation "UkrAVTO". It is proposed a procedure for the preparation and adoption of the budget. The principles and criteria for the development and use of financial planning by the companies are determined. The financial planning, as a key link of all process of planning, does it possible to incarnate strategic intentions of enterprise through development of concrete plans of operating in a plan period. Their tasks are as the following ones: to expect efficiency of present resources, to calculate the eventual economic and financial results. The systems of planning and financial management of enterprises will allow realizing the mechanism of management charges. It will promote manageability of enterprises of automobile industry. It will also create pre-conditions for charges control ensuring on the enterprise, optimize the flows of payment means, and predict the financial results and financial and economic state of corporation.

Enterprise financial risks are analyzed utilizing the theory of organizational behavior, and a financial risk management system is constructed to improve the design and algorithm of the enterprise risk management system. Base on the CCER (China Center for Economic Research) database, the early warning model for enterprise financial risk management containing five indices is proposed for enterprises. Through Logistic regression analysis, the design principle of the financial risk management system based on AI (Artificial Intelligence) technology is explained. The proposed system innovatively introduces the AI integrated learning method, optimizes objective function through XGBoost (eXtreme Gradient Boosting) algorithm, and trains the model through BP (Backpropagation) NN (Neural Network). Finally, following comparative analysis, the effectiveness of the proposed method is verified.

To develop and test decision tree (DT) models to classify physical activity (PA) intensity from accelerometer output and Gross Motor Function Classification System (GMFCS) classification level in ambulatory youth with cerebral palsy (CP) and compare the classification accuracy of the new DT models to that achieved by previously published cut points for youth with CP. Youth with CP (GMFCS levels I-III) (N = 51) completed seven activity trials with increasing PA intensity while wearing a portable metabolic system and ActiGraph GT3X accelerometers. DT models were used to identify vertical axis (VA) and vector magnitude (VM) count thresholds corresponding to sedentary (SED) (<1.5 METs), light-intensity PA (LPA) (≥1.5 and <3 METs) and moderate-to-vigorous PA (MVPA) (≥3 METs). Models were trained and cross-validated using the "rpart" and "caret" packages within R. For the VA (VA_DT) and VM DT (VM_DT), a single threshold differentiated LPA from SED, whereas the threshold for differentiating MVPA from LPA decreased as the level of impairment increased. The average cross-validation accuracies for the VC_DT were 81.1%, 76.7%, and 82.9% for GMFCS levels I, II, and III. The corresponding cross-validation accuracies for the VM_DT were 80.5%, 75.6%, and 84.2%. Within each GMFCS level, the DT models achieved better PA intensity recognition than previously published cut points. The accuracy differential was greatest among GMFCS level III participants, in whom the previously published cut points misclassified 40% of the MVPA activity trials. The GMFCS-specific cut points provide more accurate assessments of MVPA levels in youth with CP across the full spectrum of ambulatory ability.