Financial Indicator System Based on Data Mining Algorithm

Liu, Jianing

doi:10.1109/tocs56154.2022.10016014

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Through empirical analysis, the prediction accuracy of this model was 97.7% [5]. Liu designed a financial index analysis system based on data mining technology and found through experimental research that the application of this system improved the efficiency of enterprise management decision-making and saved cost for the enterprise [6]. Taking the accounts receivable of enterprises as the starting point, they studied the game strategies, possible optimal decisions, and corresponding profits among suppliers, manufacturers, and banks.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Optimization Model of Enterprise Financial Account Receivable Management

Peng,

Tian

2024

Journal of Applied Mathematics

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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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Section: Related Workmentioning

confidence: 99%