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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. 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.
    2. 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.
    3. 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.
    4. 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.
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    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. Mohamad Adzha Haiqal Hassan & Muhamad Aliff Iskandar Azmi & Muhammad Farhan Izzuddin Iskandar & Muhammad Imran Kamaruzzaman & Muhammad Shukri Zazaly & Nor Adila Zulkifli & Muthyaah Mohd Jamil, 2025. "Guardians in the Flood: Unleashing the Roles of Internal Auditors’ and Benefits," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(8), pages 2743-2751, August.
    3. 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.
    4. 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.
    5. Fekadu Agmas Wassie & László Péter Lakatos, 2024. "Artificial intelligence and the future of the internal audit function," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    6. Eulerich, Marc & Huang, Qing & Pawlowski, Justin & Vasarhelyi, Miklos A., 2025. "Using process mining as an assurance tool in the three-lines-model," International Journal of Accounting Information Systems, Elsevier, vol. 56(C).
    7. Föhr, Tassilo L. & Reichelt, Valentin & Marten, Kai-Uwe & Eulerich, Marc, 2025. "A Framework for the Structured Implementation of Process Mining for Audit Tasks," International Journal of Accounting Information Systems, Elsevier, vol. 56(C).

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