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How active learning and process mining can act as Continuous Auditing catalyst

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  • Jans, Mieke
  • Hosseinpour, Marzie

Abstract

In the context of Continuous Auditing, different approaches have been proposed to incorporate data analytics to accomplish a continuous audit environment. Some work suggests the use of data mining, some the use of process mining; some work reports on concrete case studies, where other work presents a conceptual approach. In this paper, we present an actionable framework to address one specific level of continuous auditing: the transaction verification level. This framework combines the techniques of data mining and process mining on one hand, and includes the auditor as a human expert to deal with the typical alarm flood on the other hand. Further, different research opportunities are identified in this context.

Suggested Citation

  • Jans, Mieke & Hosseinpour, Marzie, 2019. "How active learning and process mining can act as Continuous Auditing catalyst," International Journal of Accounting Information Systems, Elsevier, vol. 32(C), pages 44-58.
    • Handle: RePEc:eee:ijoais:v:32:y:2019:i:c:p:44-58
    DOI: 10.1016/j.accinf.2018.11.002
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    References listed on IDEAS

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    1. Anindya Datta, 1998. "Automating the Discovery of AS-IS Business Process Models: Probabilistic and Algorithmic Approaches," Information Systems Research, INFORMS, vol. 9(3), pages 275-301, September.
    2. Werner, Michael, 2017. "Financial process mining - Accounting data structure dependent control flow inference," International Journal of Accounting Information Systems, Elsevier, vol. 25(C), pages 57-80.
    3. Chan, David Y. & Vasarhelyi, Miklos A., 2011. "Innovation and practice of continuous auditing," International Journal of Accounting Information Systems, Elsevier, vol. 12(2), pages 152-160.
    4. Jans, Mieke & Alles, Michael & Vasarhelyi, Miklos, 2013. "The case for process mining in auditing: Sources of value added and areas of application," International Journal of Accounting Information Systems, Elsevier, vol. 14(1), pages 1-20.
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    Cited by:

    1. Yoon, Kyunghee & Liu, Yue & Chiu, Tiffany & Vasarhelyi, Miklos A., 2021. "Design and evaluation of an advanced continuous data level auditing system: A three-layer structure," International Journal of Accounting Information Systems, Elsevier, vol. 42(C).
    2. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    3. Andrea Cardoni & Evgeniia Kiseleva & Francesco De Luca, 2020. "Continuous auditing and data mining for strategic risk control and anticorruption: Creating “fair” value in the digital age," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3072-3085, December.
    4. Fekadu Agmas Wassie & László Péter Lakatos, 2024. "Artificial intelligence and the future of the internal audit function," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.

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