大数据智能化财务共享下的应收账款优化管理  

Accounts receivable optimization management under big data intelligent financial sharing

作　　者：王飞洋[1] 郭凤华[2] WANG Fei-yang;GUO Feng-hua(School of Finance and Accounting,Anhui Vocational College of Finance and Trade,Hefei 230601,Anhui,China;School of Accounting,Anhui University of Finance and Economics,Bengbu 233030,Anhui,China)

机构地区：[1]安徽财贸职业学院财会金融学院,安徽合肥230601 [2]安徽财经大学会计学院,安徽蚌埠233030

出　　处：《贵阳学院学报（自然科学版）》2021年第4期55-59,共5页Journal of Guiyang University：Natural Sciences

基　　金：2019年安徽省职业与成人教育学会教育科研规划课题“‘一带一路’背景下安徽省职业教育国际化发展趋势研究”(项目编号:Azcj092);2019年安徽省质量工程“教师教学创新团队——会计专业创新教学团队”(项目编号:2019cxtd090)。

摘　　要：随着云会计、大数据的出现,财务智能时代到来,大数据智能化财务共享成为企业进行财务管理的重要途径。应收账款管理在企业财务管理中占据重要位置,研究提出以K-means算法、BP神经网络算法结合RPA技术对企业的应收账款管理进行优化,借此提高企业应收账款管理效率及管理质量。研究结果显示,应收账款风险预测模型预测准确率达80%;随着优化后的应收账款管理模式的运行,H企业年末收账款总额减少了13.2%,应收账款余额占流动资产总额比率降至28.6%,年末资产总额提升了3.8%,应收账款周转率由8.3次升至9.5次。即研究提出的大数据智能化财务共享下的应收账款优化管理,能够明显提高企业的应收账款管理效率。以期对H集团财务共享中心智能化转型提供参考。With the emergence of cloud accounting and big data and the arrival of financial intelligence era,big data intelligent financial sharing has become an important way for enterprises to carry out financial management.Accounts receivable management plays an important role in the financial management of enterprises.This paper proposes to optimize the accounts receivable management of enterprises with k-means algorithm,BP neural network algorithm and RPA technology,so as to improve the efficiency and quality of accounts receivable management.The results show that the accuracy rate of the accounts receivable risk prediction model is 80%;with the operation of the optimized accounts receivable management mode,the total amount of accounts receivable at the end of the year has decreased by 13.2%,the ratio of the balance of accounts receivable to the total current assets has decreased to 28.6%,the total assets at the end of the year has increased by 3.8%,and the turnover rate of accounts receivable has increased from 8.3 times to 9.5 times.In other words,the optimization management of accounts receivable under the big data intelligent financial sharing can significantly improve the efficiency of accounts receivable management of enterprises.It is expected that this paper can provide reference for the financial transformation of H group.

关 键 词：BP神经网络算法 K-MEANS聚类算法 应收账款管理 风险等级 信用等级 RPA技术 

分 类 号：F232[经济管理—会计学]