Intelligent Optimization Model of Enterprise Financial Account Receivable Management
As a key component of enterprise assets, accounts receivable play an important role in enterprise financial management and determine the long-term development of enterprises in the later period. In order to minimize the financial risk brought by the credit sales of enterprises, this subject studies...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2024-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2024/4961081 |
| _version_ | 1797326340231266304 |
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| author | Yunxiang Peng Guixian Tian |
| author_facet | Yunxiang Peng Guixian Tian |
| author_sort | Yunxiang Peng |
| collection | DOAJ |
| description | As a key component of enterprise assets, accounts receivable play an important role in enterprise financial management and determine the long-term development of enterprises in the later period. In order to minimize the financial risk brought by the credit sales of enterprises, this subject studies the intelligent optimization of enterprise financial account receivable management. BP neural network and K-means clustering algorithm are used to evaluate the risk of account receivable and the owner’s credit, respectively. The account balance accounts for 45.20% of the total amount, and the risk rating of accounts receivable is 4. The training result of BP neural network algorithm has high accuracy. With K-means clustering algorithm, accurate evaluation of owner’s credit can be achieved, which can provide reference for optimization of enterprise account receivable management mode. |
| first_indexed | 2024-03-08T06:22:53Z |
| format | Article |
| id | doaj.art-faade9a7ae914153a0f3330afbda1e63 |
| institution | Directory Open Access Journal |
| issn | 1687-0042 |
| language | English |
| last_indexed | 2024-03-08T06:22:53Z |
| publishDate | 2024-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj.art-faade9a7ae914153a0f3330afbda1e632024-02-04T00:00:11ZengHindawi LimitedJournal of Applied Mathematics1687-00422024-01-01202410.1155/2024/4961081Intelligent Optimization Model of Enterprise Financial Account Receivable ManagementYunxiang Peng0Guixian Tian1School of Finance and EconomicsSchool of BusinessAs a key component of enterprise assets, accounts receivable play an important role in enterprise financial management and determine the long-term development of enterprises in the later period. In order to minimize the financial risk brought by the credit sales of enterprises, this subject studies the intelligent optimization of enterprise financial account receivable management. BP neural network and K-means clustering algorithm are used to evaluate the risk of account receivable and the owner’s credit, respectively. The account balance accounts for 45.20% of the total amount, and the risk rating of accounts receivable is 4. The training result of BP neural network algorithm has high accuracy. With K-means clustering algorithm, accurate evaluation of owner’s credit can be achieved, which can provide reference for optimization of enterprise account receivable management mode.http://dx.doi.org/10.1155/2024/4961081 |
| spellingShingle | Yunxiang Peng Guixian Tian Intelligent Optimization Model of Enterprise Financial Account Receivable Management Journal of Applied Mathematics |
| title | Intelligent Optimization Model of Enterprise Financial Account Receivable Management |
| title_full | Intelligent Optimization Model of Enterprise Financial Account Receivable Management |
| title_fullStr | Intelligent Optimization Model of Enterprise Financial Account Receivable Management |
| title_full_unstemmed | Intelligent Optimization Model of Enterprise Financial Account Receivable Management |
| title_short | Intelligent Optimization Model of Enterprise Financial Account Receivable Management |
| title_sort | intelligent optimization model of enterprise financial account receivable management |
| url | http://dx.doi.org/10.1155/2024/4961081 |
| work_keys_str_mv | AT yunxiangpeng intelligentoptimizationmodelofenterprisefinancialaccountreceivablemanagement AT guixiantian intelligentoptimizationmodelofenterprisefinancialaccountreceivablemanagement |