Construction of Enterprise Financial Data Visualization Analysis System Based on Data Mining Technology

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

Modern critical care medicine is based on flow of information. This information requires significant amount of interpretation to become clinically valuable. Despite the wealth of data in the modern Intensive Care Units (ICUs), intensivists often rely on fragmentary information. In an attempt to improve data trending utilization, application of financial analysis (FA) methods to vital sign data samples was examined. Two randomly chosen vital sign datasets of patients who spent at least 30 days in the ICU were retrospectively reviewed. Hourly vital sign data were retrieved and recorded for each patient. Variables tracked included systolic blood pressure for patient #1 and heart rate for patient #2. These variables were then entered into specialized FA software and subjected to computer-based processing. Trends in the recorded data were examined using (1) the stochastic oscillator (SO), (2) the moving average (MAV) convergence-divergence tool, (3) price envelope analysis and (4) MAV analysis. Both blood pressure and heart rate analyses demonstrated that vital sign data could be successfully trended using FA techniques. Not only was the vital sign data easy to read and interpret when formatted in financial-like fashion, but some trends that were not apparent on gross inspection of the numeric data were clearly demonstrated on FA. Much like with financial patterns, trends noted within vital sign data appeared to be more significant when more than one indicator identified them, utilizing the concept of a confirmatory variable. Vital sign data, much like financial data, were subject to trend reversals. Such reversals in vital sign data appeared to follow rules similar to those followed by financial vehicles and markets. This report demonstrates that vital sign data can be subjected to the same manipulations as financial market data. Furthermore, FA tools appear to provide the interpreter of the data with means to define, confirm, and possibly predict trends and trend reversals.The following core competencies are addressed in this article: Medical knowledge, Practice-based learning and improvement, Systems-based practice.Republished with permission from: Stawicki SP. Application of financial analysis techniques to vital sign data – A novel method of trend interpretation in the Intensive Care Unit. OPUS 12 Scientist 2007;1(1):14-16.

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 article is devoted to the impact of artificial intelligence (AI) on financial management system, efficiency and feasibility of introducing mechanisms, technologies and programs based on artificial intelligence into the work of a financial structure. It describes the essence of artificial intelligence and its features. The article uses the following research methods: analysis, synthesis, observation, measurement, comparison, financial analysis, as well as data processing and interpretation. The relevance and scientific novelty of research consists in the analysis and determination of the relationship between the key performance indicators of financial management and the use of mechanisms, technologies and programs based on artificial intelligence; identification of the degree of influence of technologies based on artificial intelligence on the effectiveness of the financial management system of commercial organizations. A scientific research on the influence of AI on the financial management system in a commercial organization is conducted using the example of PJSC Magnit, the largest retail chain of stores founded in Krasnodar. The data for the study were taken from the official website of the organization and from the financial and non-financial reports and documents provided therein. The degree of digitalization of the company, the systems used based on artificial intelligence are studied, and the work of the company’s financial management system is analyzed. The efficiency of the financial system is studied, the indicators related to the company’s financial management system are analyzed. The relationship between investments in the creation of IT departments, AI systems and their use in the financial system with the company’s financial indicators is revealed. A conclusion is made about the positive impact of the implementation of mechanisms using artificial intelligence in the work of the financial management system to achieve its goals and maintain competitiveness. The measures are suggested for commercial organizations on the methods and forms of the use of artificial intelligence system.

The role of information technology is the main focus in financial management, especially in supporting the data analysis process for effective decision making. Village Credit Institution xyz, as a financial institution owned by Pekraman Villages in Bali, faces obstacles in presenting financial data that is still in the form of tables. The presentation is not effective in providing a clear and adequate picture to support management decision-making. Makes it difficult to identify customers with bad or current credit. This research aims to build a financial data visualization system at Village Credit Institution xyz using Looker Studio, with a data warehouse design implemented using Kimball's Nine Steps method. The ETL process is carried out using Pentaho Data Integration (PDI) to compile data from various sources. The final result of this research is data visualization in the form of 9 main menus. This system allows the presentation of data in the form of interactive graphs, thus facilitating data analysis, accelerating the decision-making process, and increasing the efficiency of financial data management in Village Credit Institution. System testing was conducted using the User Acceptance Testing (UAT) method with a result of 92.48% or strongly agree, indicating that the developed system has met the needs of users.

The worldwide economic and financial systems' development, growth, and expansion arecalled global financial growth. It refers to global, national, and regional economic growth, trade, and investment. One can assess the global economy by tracking Gross Domestic Product (GDP), international trade, employment, and other financial variables. In this research, investigate the deep learning (DL) models for forecasting and regulating global financial growth. Governments and economists consider a global financial growth as a sign of prosperity, stability, and rising living standards. Accurately predicting and controlling the expansion of the global financial system requires an awareness of these interconnections and their possible effects. Bank runs and stock market crashes are the two examples of the kinds of crises that pressure the international financial system. The capacity to anticipate and manage these crises is critical for sustained global financial expansion. In this study, we suggested a Distributed Sailfish Optimized Hybrid Improved Artificial Neural Network (DSO-HIANN), which has shown promise in tackling issues in global financial growth. The research collected financial data. Here the logistics data is collected and preprocessed using z-score normalization. We employed Principal Component Analysis (PCA) method to extract data features in financial data. Governments, financial institutions, investors, and a politician requires an accurate financial growth forecasts. Our suggested approach, DSO-HIANN, outperformed than other existing methods in the context of f1-score (98.70), recall (98.80%), precision (98.02%), and accuracy (97.90%).Financial analysis that employs a DL algorithms has the potential to enhance accuracy and sophisticated when applied to the challenge of anticipating and managing global financial growth.

This study aimed to identify the impact of artificial intelligence on strategic decision-making through financial analysis as a mediating variable, relying on the descriptive analytical approach. The study population consisted of top-level management in Jordanian commercial banks, and the sample size was (213). Descriptive and statistical analysis SPSS-22 was used, and the study concluded that the level of application of artificial intelligence in Jordanian commercial banks was moderate, and the level of practice of strategic decision-making was moderate. Based on the results, the study recommended enhancing investment in artificial intelligence technologies and developing their use in financial analysis to support the accuracy and efficiency of strategic decisions, with a focus on integrating them into decision-making processes instead of relying solely on traditional financial analysis

"The new source of power is not money in the hands of a few, but information in the hands of many." The aforementioned quote from John Naisbitt seems to be even more relevant in the world of finance at this very moment. Many financial decisions come from watching the information stream, selecting relevant data, analyzing it and acting accordingly. With the increasing global competition, the need for swift data analysis, high accuracy and quality becomes a must. XBRL (Extensible Business Reporting Language)XBRL: http://www.xbrl.org/ standard was proposed to improve efficiency of data exchange in the financial domain. However; it is still struggling with interoperability problems, not to mention comparability of data or multisource data integration. This paper presents the FLORA intelligent platform: an approach for dealing with current financial information shortcomings and achieving more effective way of processing financial data based on the Linked Data principles. The article also explains the process of data extraction and semantic modeling which are the cornerstones of efficient financial data analysis. As a result, the FLORA architecture facilitates effective, data-driven, financial analyses and Web-scale integration between financial applications and platforms.

In view of the defect of the traditional financial analysis system that only focuses on the surface data analysis of financial statements and ignores the evaluation of the core capabilities of enterprises, this paper attempts to construct financial analysis indicators for core competitiveness to improve the deficiencies of the traditional financial analysis system. Based on a systematic review and summary of the research literature and development environment of core competitiveness at home and abroad, this paper reconstructs the financial analysis indicator system of core competitiveness from four dimensions: return on human capital investment, R&D innovation ability, cash balance ability, and capital investment return rate. At the same time, the empirical results based on the data of listed companies in Shaanxi Province show that there is a positive correlation between the core competitiveness of enterprises and the four financial analysis indicators, indicating that the constructed indicators have certain rationality and practical value.

This paper conducts a methodological research on the academic performance of online learners. This paper applies classification models commonly used in data mining algorithms, such as random forests based on decision trees, support vector machines, neural networks, K nearest neighbor algorithms, etc., combined with data mining tools S software and statistical analysis tools, and analyzes the University of Finance and Economics Tools scores of online learners in the class of 2019. It studies the important factors that affect the academic performance of college students' online learners, and uses these factors to predict students' academic performance. Based on the distance calculation method in mathematics, the article separately studied the application of Euclidean distance correlation analysis algorithm and correlation coefficient correlation algorithm in curriculum relevance, and compared several correlation algorithms. Experimental research results show that in the era of big data, learners will accumulate a large amount of structured and unstructured data during online learning. We can explore the influencing factors of online learners' academic performance through data mining technology, and we can also use machine learning to automatically learn from the data to the academic performance prediction model.

Financial Big Data is a contentious topic that needs to be addressed because it is having an increasingly negative impact on people's everyday lives, work habits, and thought patterns. The leakage of financial big data is just one of the issues that financial big data is now dealing with. As a result, the study focus of this work is on the data security problems and solutions related to financial big data. This paper summarizes four main categories of data security issues related to financial big data: attacks on financial big data, data leaks related to financial big data, issues with the reliability of financial big data, and issues with access control related to financial big data. Based on these four problem categories, it searches the literature and organizes the suggested solutions as follows: pay attention to the development of financial data security systems in a big data environment, strengthen education and training, increase the da-ta security awareness of various roles, optimize the processing of big data, and strengthen the storage security of big data by strict user access control. This paper's thought and research also reveal that while financial big data does provide data security issues, it also serves as the primary means of addressing the issue itself. Less research has been done on using financial big data as a countermeasure in and of itself. In the future, we should focus on financial big data itself and explore ways to use financial big data as a countermeasure to data security problems.

The use of traditional financial statements and historical experience by enterprise managers cannot meet the needs of informationization in university financial management. Based on the research and analysis of enterprise financial data, and integrated with data mining technology this article designs a decision tree model based enterprise financial management and decision-making system. In the intelligent decision-making system, certain optimization improvements are proposed for the continuous attribute discretization method in C4.5 algorithm, adjusting the range of attribute metric information gain rate. Then, using the mean value theorem and Maclaurin approximation, the modified information gain rate equation is logarithmically simplified, and a improved decision tree model is constructed to achieve financial management and decision-making in universities. Finally, the feasibility and effectiveness of the solution are verified through the specific implementation and testing process of the system. The experimental results also indicate that the improved C4.5 decision tree greatly improves execution efficiency and reduces demand space compared to traditional classification algorithms, and it also has high comprehensive performance.

Introduction. The current stage of economic and business development is characterized by profound transformations that affect all aspects of management, including financial analysis. The design of traditional financial analysis is changing, transforming into an integrated, digitalized and strategically oriented system that combines classical methods with modern information technologies, takes into account operational activities, market trends and behavioral factors, combining them into a comprehensive analytical model. New challenges require the transformation of the theoretical and methodological foundations of financial analysis by revising traditional approaches and integrating new ones that take into account the dynamism, uncertainty and complexity of the modern business environment, as well as current concepts, theories and practical challenges. Purpose of the study. The purpose of the article is to analyze the main trends in financial analysis in the context of the modern management paradigm, the convergent evolution of which transforms the design and outlines the priority concepts of the new general structure of financial analysis. Methods. To achieve the outlined goal, a set of scientific research methods was used, providing a comprehensive analysis of the latest paradigm of financial analysis and management. In particular: the method of theoretical generalization was used to systematize scientific approaches and concepts; comparative analysis was used to evaluate traditional and modern methodologies of financial analysis; a systemic approach; the method of structural-functional analysis contributed to the study of the relationships between the components of financial analysis in the context of its transformation; inductive and deductive methods. Results. The article outlines modern trends and conceptual changes in financial analysis in the context of the latest management paradigm. The need for its transformation through the integration of traditional approaches with modern methods that take into account digital transformation, ESG factors, behavioral economics and algorithmic analysis of big data is proven. The key concepts of the new general framework of financial analysis are considered, in particular methodological triangulation, enterprise life cycle analysis, behavioral approach, predictive orientation and financial security. The importance of innovative technologies, such as machine learning, blockchain, cognitive analysis and cloud computing, in increasing the flexibility and efficiency of financial analysis is highlighted. It is substantiated that the convergence of financial analysis with the latest concepts contributes to its adaptation to the conditions of uncertainty of the modern market, ensures the efficiency of management decision-making and forms the long-term financial sustainability of enterprises. Conclusions. Thus, modern financial analysis requires transformation through the integration of innovative approaches that take into account digital transformation, ESG factors, behavioral aspects and big data analytics. This ensures its flexibility, predictiveness and adaptability to conditions of uncertainty, contributing to the stability and efficiency of management decisions in a dynamic business environment. Prospect. The lack of a complete, systematized methodology of financial analysis is a serious problem that limits its effectiveness and practical value. To overcome this problem, it is necessary to develop a new, modern approach that will integrate financial and non- financial indicators and factors, take into account the current challenges of modern times and will be aimed at supporting management decision-making taking into account the interests of all interested parties. This methodology will improve the quality of financial analysis, ensure its adaptation to modern conditions and turn it into a powerful business management tool.

In response to the heightened demands placed on accounting professionals in the era of big data, and the need to cultivate students’ capacities in big data analysis and digital literacy, this study proposes an innovative approach to teaching financial big data analysis courses rooted in the Outcome-based Education (OBE) framework. Addressing prevalent issues in current teaching practices, such as inadequate alignment with real-world business scenarios, students’ lack of a strong sense of purpose in their learning, and rigid assessment methods, the research undertakes a thorough optimization of the course structure. By carefully considering institutional context, student requirements, and market dynamics, the study refines course objectives, reconfigures instructional content and resources, and advocates for teaching mode innovation and assessment reform. Resulting from these efforts is a comprehensive teaching model that seamlessly integrates “theoretical exposition, practical business scenario analysis, hands-on Python demonstrations, and task-oriented exercises”. Grounded in a pedagogical logic that emphasizes an instructional approach “digging deep into a single case”, this model significantly enhances students’ proficiency in accounting practices, data analysis techniques, business acumen, and data literacy.

With the rapid development of Internet technology and wide use of computer, digitalization also gets large growth attributes to the surprisingly fast development of science and technology at college. The data sharing has become the bottleneck of digital campus development between the financial system and the use of digital campus data files. In this paper, we make the analysis financial data of the financial system and the digital campus network data sharing, automated processing strategies, and achieve the financial data subscription, automated data extraction. Combining financial system and informatization together with the purpose of realizing a complete sharing of financial information, we have designed the college financial management system, which includes the subsystems: account management, student fees management, compensation management and so on. This paper also makes firstly introduction of the technology adopted in the system and explain the function of each subsystem.