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

Abstract

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.

The Current State and Challenges of Financial Risk Management

“…On a positive note, globalization has also brought opportunities for diversification and access to a broader pool of investment options. International markets offer the potential for higher returns and portfolio diversification, but they also come with unique risks and complexities that must be carefully managed [6].…”

Digital finance and regional systemic financial risk forecasting

“…First, digital finance can exacerbate competition in the banking sector and increase the rate of non-performing loans [13]. The development of digital finance has seized the market share of the traditional business of commercial banks, and in order to cope with the competition, traditional commercial banks are forced to reduce the risk control requirements to pull in customers, which exacerbates the financial risk [14][15]. Second, digital finance default risk is higher.…”

“…Enlin Tang, Chunsong Bai and Teng Qin. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] …”

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

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”