The Integration of Financial Business and the Transformation of Financial Management Functions Based on Internal Control Optimization Algorithm

被引：0
作者：
Zhao R. [1 ]
Tang D. [2 ]
机构：
[1] Department of Economics and Management, Cangzhou Normal University, Hebei, Cangzhou
[2] Department of Physics and Information Engineering, Cangzhou Normal University, Hebei, Cangzhou
来源：
Informatica (Slovenia) | 2024年 / 48卷 / 10期
关键词：
business integration; corporate finance; financial management; internal control optimization;
D O I：
10.31449/inf.v48i10.5672
中图分类号：
学科分类号：
摘要：
The financial and business integration capability of an enterprise is an important part of the financial management capability of an enterprise. Based on the internal control optimization algorithm, this paper constructs a model of enterprise financial business integration and financial management, mainly by combining the principal component analysis principle and the artificial network, to analyze and obtain the financial management index system of regional listed companies. The model uses component analysis and internal control optimization algorithm to manage the network model, improves the input data of the forecast model, and uses the internal control algorithm to search for initial weights and thresholds for the management network, which solves the problem of the accuracy of financial management forecasts. In the simulation process, according to the idea of cross-validation, this paper selects the ratio of the number of samples in the training set and the test set to be 25: 10. It divides the research sample data into two categories: a training set and test set, which are respectively used for the training of the optimized control network model. Taking the financial index data of listed companies in the region as a sample set, the results show that the training score of the internal control optimization algorithm is 100. That is, the fit of the training data is 100%, and the test score is 78.95. The output results reflect the financial management evaluation status, effectively improving prediction accuracy. © 2024 Slovene Society Informatika. All rights reserved.
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页码：165 / 180
页数：15
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