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Embedding process mining into financial statement audits

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  • Werner, Michael
  • Wiese, Michael
  • Maas, Annalouise

Abstract

The audit of financial statements is a complex and highly specialized process. Digitalization and the increasing automation of transaction processing create new challenges for auditors who carry out those audits. New data analysis techniques offer the opportunity to improve the auditing of financial statements and to overcome the limitations of traditional audit procedures when faced with increasingly large amounts of financially relevant transactions that are processed automatically or semi-automatically by computer systems. This study discusses process mining as a novel data analysis technique which has been receiving increased attention in the audit practice. Process mining makes it possible to analyse business processes in an automated manner. This study investigates how process mining can be integrated into contemporary audits by reviewing the relevant audit standards and incorporating the results from a field study. It demonstrates the feasibility of embodying process mining within financial statement audits in accordance with contemporary audit standards and generally accepted audit practices. Implementation of process mining increases the reliability of the audit conclusions and improves the robustness of audit evidence by replacing manual audit procedures. Process mining as novel data mining technique provides auditors the means to keep pace with current technological developments and challenges.

Suggested Citation

  • Werner, Michael & Wiese, Michael & Maas, Annalouise, 2021. "Embedding process mining into financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).
    • Handle: RePEc:eee:ijoais:v:41:y:2021:i:c:s1467089521000166
    DOI: 10.1016/j.accinf.2021.100514
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    References listed on IDEAS

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    1. Baader, Galina & Krcmar, Helmut, 2018. "Reducing false positives in fraud detection: Combining the red flag approach with process mining," International Journal of Accounting Information Systems, Elsevier, vol. 31(C), pages 1-16.
    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. Bierstaker, James & Janvrin, Diane & Lowe, D. Jordan, 2014. "What factors influence auditors' use of computer-assisted audit techniques?," Advances in accounting, Elsevier, vol. 30(1), pages 67-74.
    4. Kim, Hyo-Jeong & Mannino, Michael & Nieschwietz, Robert J., 2009. "Information technology acceptance in the internal audit profession: Impact of technology features and complexity," International Journal of Accounting Information Systems, Elsevier, vol. 10(4), pages 214-228.
    5. 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. Daehyoun Choi & Hind R’bigui & Chiwoon Cho, 2021. "Candidate Digital Tasks Selection Methodology for Automation with Robotic Process Automation," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    2. Ruhnke, Klaus, 2023. "Empirical research frameworks in a changing world: The case of audit data analytics," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 51(C).
    3. Matheus Camilo da Silva & Gabriel Marques Tavares & Marcos Cesar Gritti & Paolo Ceravolo & Sylvio Barbon Junior, 2023. "Using Process Mining to Reduce Fraud in Digital Onboarding," FinTech, MDPI, vol. 2(1), pages 1-18, February.
    4. Chen, Qianqian & Chen, Zhi, 2024. "Mandatory internal control audit and corporate financialization," Finance Research Letters, Elsevier, vol. 62(PA).

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