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<p>Exploring the architecture of corporate financial management objectives of the BRP method is to help companies achieve revenue growth. In this paper, we analyze the motivation for implementing business process reengineering and constructing an intelligent financial management system based on the BRP method and intelligent technology. The DSBPR data analysis and processing model is constructed by using Bayesian ranking model combined with similarity in heterogeneous information networks in the intelligent financial management system. The intelligent financial management system constructed in this paper is analyzed in terms of the operating capacity and profitability of the enterprise. From the viewpoint of operating capability, the turnover rate of receivables, total assets, and current assets decreased by 87.17%, 91.85% and 49.68%, respectively in ten years, and the change rate of inventory turnover was 7.91%. In terms of profitability, there is a difference of 166.12% between the maximum and minimum values of return on net assets. This shows that the use of an intelligent financial management system can provide intuitive data analysis for enterprise development, which helps enterprises to target the coordinated development of enterprise strategies and thus promote the improvement of economic returns.</p>
</abstract>

Exploring the architecture of corporate financial management objectives of the BRP method is to help companies achieve revenue growth. In this paper, we analyze the motivation for implementing business process reengineering and constructing an intelligent financial management system based on the BRP method and intelligent technology. The DSBPR data analysis and processing model is constructed by using Bayesian ranking model combined with similarity in heterogeneous information networks in the intelligent financial management system. The intelligent financial management system constructed in this paper is analyzed in terms of the operating capacity and profitability of the enterprise. From the viewpoint of operating capability, the turnover rate of receivables, total assets, and current assets decreased by 87.17%, 91.85% and 49.68%, respectively in ten years, and the change rate of inventory turnover was 7.91%. In terms of profitability, there is a difference of 166.12% between the maximum and minimum values of return on net assets. This shows that the use of an intelligent financial management system can provide intuitive data analysis for enterprise development, which helps enterprises to target the coordinated development of enterprise strategies and thus promote the improvement of economic returns.

Research on the structure of corporate financial management objective system based on BRP method

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the architecture of corporate financial management objectives of the BRP method is to help companies achieve revenue growth. In this paper, we...

Research on the structure of corporate financial management objective system based on BRP method

Publicado en línea: 04 oct 2023

Páginas: -

Recibido: 13 nov 2022

Aceptado: 14 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00498

Palabras clave
Intelligent financial management, Heterogeneous information networks, Bayesian ranking, DSBPR model

© 2023 Baozhen Han, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the structure of corporate financial management objective system based on BRP method

Publicado en línea: 04 oct 2023

Páginas: -

Recibido: 13 nov 2022

Aceptado: 14 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00498

Palabras claveIntelligent financial management, Heterogeneous information networks, Bayesian ranking, DSBPR model

© 2023 Baozhen Han, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Intelligent financial management, Heterogeneous information networks, Bayesian ranking, DSBPR model