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<p>This paper first describes the process of the BP neural network mining algorithm, then applies the optimization algorithm to optimize the BP neural network model to improve its performance. The data mining algorithm is used to mine the financial management and supervision data of colleges and universities and analyze the problems in the financial management and supervision system of colleges and universities in terms of the financial management reimbursement process, budget process, financial information entry, and financial supervision respectively, and put forward suggestions for improvement. Regarding the awareness of financial information in colleges and universities, only about 30% of the people in each project said they understood it. In terms of financial work satisfaction, more than 50% are satisfied and very satisfied. On the problems of the reimbursement process, 87.75% of people think the approval process is long, 70.5% think the reimbursement rework rate is high, and 57.6% think the reimbursement queuing time is long. It is difficult to track the budget progress on a financial budget, lack of modern technology in the financial system, and lack of budget supervision are the top three, accounting for 77.3%, 77.5%, and 69.6%, respectively. Financial management in universities should keep up with the times and actively reform for the existing problems.</p>
</abstract>

This paper first describes the process of the BP neural network mining algorithm, then applies the optimization algorithm to optimize the BP neural network model to improve its performance. The data mining algorithm is used to mine the financial management and supervision data of colleges and universities and analyze the problems in the financial management and supervision system of colleges and universities in terms of the financial management reimbursement process, budget process, financial information entry, and financial supervision respectively, and put forward suggestions for improvement. Regarding the awareness of financial information in colleges and universities, only about 30% of the people in each project said they understood it. In terms of financial work satisfaction, more than 50% are satisfied and very satisfied. On the problems of the reimbursement process, 87.75% of people think the approval process is long, 70.5% think the reimbursement rework rate is high, and 57.6% think the reimbursement queuing time is long. It is difficult to track the budget progress on a financial budget, lack of modern technology in the financial system, and lack of budget supervision are the top three, accounting for 77.3%, 77.5%, and 69.6%, respectively. Financial management in universities should keep up with the times and actively reform for the existing problems.

Improvement of the financial management supervision system of universities in the context of big data

Applied Mathematics and Nonlinear Sciences

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

This paper first describes the process of the BP neural network mining algorithm, then applies the optimization algorithm to optimize the BP neural network...

Improvement of the financial management supervision system of universities in the context of big data

Online veröffentlicht: 14. Aug. 2023

Seitenbereich: -

Eingereicht: 26. Sept. 2022

Akzeptiert: 22. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00186

Schlüsselwörter
Big data, Financial management, BP neural network, Data mining, Budget process

© 2023 Jingjing Shan, published by Sciendo

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

Improvement of the financial management supervision system of universities in the context of big data

Online veröffentlicht: 14. Aug. 2023

Seitenbereich: -

Eingereicht: 26. Sept. 2022

Akzeptiert: 22. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00186

SchlüsselwörterBig data, Financial management, BP neural network, Data mining, Budget process

© 2023 Jingjing Shan, published by Sciendo

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

Schlüsselwörter
Big data, Financial management, BP neural network, Data mining, Budget process