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...

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Main Authors: Yunxiang Peng, Guixian Tian
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2024/4961081
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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.
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spelling doaj-art-faade9a7ae914153a0f3330afbda1e632025-02-03T05:32:40ZengWileyJournal 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
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AT guixiantian intelligentoptimizationmodelofenterprisefinancialaccountreceivablemanagement