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Financial big data management and intelligence based on computer intelligent algorithm.

Jia Liu1, Shuai Fu2

  • 1School of Economics and Management, Harbin University, Harbin, 150086, Heilongjiang, China.

Scientific Reports
|April 24, 2024
PubMed
Summary
This summary is machine-generated.

Enterprise financial control faces risks from large-scale operations. Intelligent algorithms, including reverse neural networks and particle swarm optimization, are crucial for effective financial management and risk warning in big data environments.

Keywords:
Big data management and controlFinancial big dataIntelligent analysis of management and controlIntelligent computer algorithms

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Area of Science:

  • Business Administration
  • Financial Management
  • Computer Science

Background:

  • Economic integration drives enterprise scale and competition.
  • Large-scale enterprises face challenges in financial control and risk management.
  • False growth in business performance can lead to significant financial and operational risks.

Purpose of the Study:

  • To analyze enterprise financial control needs in theory and practice.
  • To design and implement financial control strategies using intelligent algorithms.
  • To evaluate the effectiveness of financial management and risk warning systems.

Main Methods:

  • Case study of ZH Group's financial situation.
  • Development of financial control modules.
  • Application of reverse neural networks for evaluation.
  • Utilization of particle swarm optimization for optimal solutions.
  • Construction of a financial risk indicator system.

Main Results:

  • ZH Group's total asset turnover rate decreased by 0.39 times over 5 years.
  • Despite adjustments, the company's operational and business capabilities require further improvement.
  • Intelligent algorithms demonstrated value in financial big data management.

Conclusions:

  • Intelligent algorithms are essential for enhancing enterprise financial control.
  • Advanced computational methods improve decision-making, introspection, and risk management.
  • The study highlights the necessity of integrating AI in financial big data analysis.