Intelligent Optimization Model of Enterprise Financial Account Receivable Management

Abstract

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.