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Data-Driven Intelligent Risk System in the Process of Financial Audit

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  • Tianheng Xie
  • Jianfang Zhang
  • Xiangtao Li

Abstract

Financial auditing is an important part of government control, and monitoring the risks of the financial system helps maintain the stability of a country’s financial system. This paper aims to study the operation of data-driven intelligent risk analysis system in the process of financial auditing. When the risk level of the financial system reaches a certain warning point, it needs to be adjusted, conduct strict audits on high-risk institutions, strengthen cooperation with financial audit institutions, and make them more systematic. Risk control within a reasonable range will neither have a significant impact on the financial system nor increase the impact on the industry. This paper proposes to integrate the internal and establish an internal control system for the financial system.. If the internal control of the financial system cannot be sustained, financial innovation will cause great damage to financial institutions if the compensation is not good. Especially in terms of access strategy, it has made a strong contribution to economic innovation. The experimental data in this paper show that more than 25% of data-driven intelligent risk analysis systems have begun to be used in the process of financial auditing. At the same time, its utilization rate is significantly increasing with the development of technology. This paper introduces the relevant content of financial auditing, analyzes the auditing systems in different fields, integrates resources, taps on multisectoral needs, establishes a cross-departmental governance system, and improves financial stability and the efficiency of a wide range of social services.

Suggested Citation

  • Tianheng Xie & Jianfang Zhang & Xiangtao Li, 2022. "Data-Driven Intelligent Risk System in the Process of Financial Audit," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, April.
  • Handle: RePEc:hin:jnlmpe:9054209
    DOI: 10.1155/2022/9054209
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