Enterprise Financial Risk Management Using Information Fusion Technology and Big Data Mining

Yue, Huabo; Liao, Haojie; Dong, Liang; Chen, Ling

doi:10.1155/2021/3835652

Cited by 24 publications

(19 citation statements)

References 34 publications

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“…Since both iK(x, x) and iK(x, x) have been calculated in the SVM process, the calculation will not increase the amount of calculation. Te specifc judgment rule is as follows: we set a certain critical point ε, substitute the sample x into equation ( 6), if its discriminant function g(x) < ε, then we calculate the difference between this sample x and all training samples through equation (7). Distance {di}, we fnd the smallest k among them and, fnally, discriminate the sample points to be discriminated according to the class discrimination rule.…”

Section: Resultsmentioning

confidence: 99%

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Discussing the Construction of a Budget Management System Combining Multimedia Technology and Financial Risk Management

Jiang

2022

Advances in Multimedia

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The development of modern technology allows enterprises to continuously expand the means of financial management. The use of advanced information technology and modern management means makes the financial management of enterprises continue to improve, but there are also many problems. In the face of various types of risks that enterprises may face at present, in addition to strengthening financial accounting, enterprises should also recognize the various risks that the double-edged sword of financial management informatization may bring to enterprise financial management and carefully analyze the reasons for the emergence of risks, from the ideological and technical means to continuously improve the entire process of financial management informatization, which is an important topic for every financial worker to discuss. This paper mainly studies and innovates the data mining process and the support vector machine model and designs the data preprocessing method in the data mining process to perform feature selection and optimize the parameters of the support vector machine model. The specific process is as follows: based on CRISP-DM, the industry-standard model of the data mining process, a series of data preprocessing, eigenvalue extraction, parameter optimization, training set pruning, and other methods are designed from business understanding, data understanding, data preparation, and other aspects to improve data quality. In the traditional support vector machine, when the test sample is located at the boundary point of the hyperplane, the judgment may be wrong. In the aspect of SVM model improvement, according to the discrimination method of SVM, the weighted K-nearest neighbor algorithm is introduced to redistinguish the qualified test samples in the feature space. From the whole process of data mining, a data mining system is designed, which is “data preprocessing standardization + genetic algorithm feature selection + training set pruning + support vector machine classifier discrimination optimization.” Finally, this paper uses a series of data mining optimization methods designed to mine the actual financial data of listed companies and has achieved good results.

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Section: Resultsmentioning

confidence: 99%

“…It is also necessary to process data and obtain predictions about future market trends [6]. It is also necessary to be satisfed [7]. Access is available to them [8].…”

Section: Introductionmentioning

confidence: 99%

Discussing the Construction of a Budget Management System Combining Multimedia Technology and Financial Risk Management

Jiang

2022

Advances in Multimedia

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“…It has unique investment management and profitable channels. e entry of venture capital is not based on the control of the company, but through the operation of the equity investment income and the transfer of the shareholding in the capital market [8].…”

Section: Evaluation Of Financial Investment Riskmentioning

confidence: 99%

Mathematical Model of Quantitative Evaluation of Financial Investment Risk Management System

Wang

2022

Mathematical Problems in Engineering

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The financial investment risk management system refers to an analysis and control of the intelligent system to invest in the lower financial situation so that investors quickly understand the situation in the financial industry. The purpose of this article is to use a digital model to evaluate financial investment risk management system. The investment risk value can be better evaluated by building a digital model. This paper first introduces financial investment risks and then elaborates the evaluation system and related digital models. The standards of the evaluation system are also given. The GARCH model is established to analyze the LME copper and LME aluminum case selected by this paper by investigation and analysis of the current status of corporate financial investment risks. The experimental results show that the evaluation results are often close to the reality when using the GARCH model evaluation of financial investment risk management system, and the accuracy is quite high. In addition to the EGARCH-N model, the established model is more accurate at 90% confidence level, which is more accurate and is relatively close to a given significance level.

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“…ose that are not excellent include four types: good, medium, qualified, and unqualified. erefore, the evaluation results of teachers can be simplified into two types: "yes" and "no" [19].…”

Section: Practical Application Of Id3 Algorithm In Teaching Quality E...mentioning

confidence: 99%

The Architecture of College Psychological Teaching Management System Based on Data Mining Technology

Huang

Leng

2022

Security and Communication Networks

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In order to meet the requirements of the development of educational informatization, combined with data mining technology, this article studies the architecture of the college psychological teaching management system. To experience the benefits brought by the Internet age, in order to further improve performance and meet the needs of the Internet information age, many colleges and universities have begun to strengthen the development of teaching management. Based on this background, this article proposes a standardized mode of mental health management in colleges and universities based on information technology. With the help of data warehouse and data mining technology, the ID3 algorithm, organizational rules, and other algorithms are used to develop a psychology subject evaluation system. Based on the improvement of grades and the behavioral characteristics of college students, this article formulates cross-platform operation rules, determines the B/S type as the normal state, and solves the best problem through comparison and selection. Then, a more specific system architecture design is carried out. The architecture of the software system selects the classic three-tier structure, which makes the client independent of the use platform and meets the requirements of user diversity and system portability. The design of functional modules such as the system fully meets the requirements of the ideological education management system for college students in the above analysis, with reasonable structure and clear logic. Finally, the advantages of the designed system architecture are summarized, including clarifying the grading idea of ideological education and training. It expands the transplantation ability of the system in a cross-platform way.

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Enterprise Financial Risk Management Using Information Fusion Technology and Big Data Mining

Cited by 24 publications

References 34 publications

Discussing the Construction of a Budget Management System Combining Multimedia Technology and Financial Risk Management

Discussing the Construction of a Budget Management System Combining Multimedia Technology and Financial Risk Management

Mathematical Model of Quantitative Evaluation of Financial Investment Risk Management System

The Architecture of College Psychological Teaching Management System Based on Data Mining Technology

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