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Ï»¿Audit Digitalization And Data Mining

Author

Listed:
  • Andrada LASCÄ‚U

    (West University of TimiÅŸoara)

Abstract

In the context of continuous change and business transformation, the audit and financial team are in constant motion to best serve the tasks they have to perform, such as exploitation of recently created low cost, use of new and emerging technologies such as robotics and artificial intelligence, also changing applications and automating processes.In a fast-growing market as software development, open source applications are a growing trend by providing low costs and choices. The need for audit tools is important. This paper establishes the improvement of audit through digitalization which apply data mining technique to improve financial reporting requirements in the context of electronic data collection.Exploiting data helps organizations focus on the most important information and knowledge available in the entire database.All of these activitiesof data mining will help leading to efficiency and cost and also critically free up capacity and resources to provide more efficient data on management information and improved business decision supported by processed data.

Suggested Citation

  • Andrada LASCÄ‚U, 2022. "Ï»¿Audit Digitalization And Data Mining," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 1(24), pages 1-10.
    • Handle: RePEc:alu:journl:v:1:y:2022:i:24:p:10
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    References listed on IDEAS

    as
    1. Granlund, Markus, 2011. "Extending AIS research to management accounting and control issues: A research note," International Journal of Accounting Information Systems, Elsevier, vol. 12(1), pages 3-19.
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    More about this item

    Keywords

    audit; digitalization; software; data mining; audit tools;
    All these keywords.

    JEL classification:

    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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