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The case for process mining in auditing: Sources of value added and areas of application

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  • Jans, Mieke
  • Alles, Michael
  • Vasarhelyi, Miklos

Abstract

Process mining aims to extract knowledge from the event logs maintained by a company's ERP system. The objective of this paper is to make the case for why internal and external auditors should leverage the capabilities process mining offers to rethink how auditing is carried out. We do so by identifying the sources of value added of process mining when applied to auditing, which are as follows: 1. process mining analyzes the entire population of data and not just a sample; 2. critically that data consists of meta-data—data entered independently of the actions of auditee—and not just data entered by the auditee; 3. process mining allows the auditor to have a more effective way of implementing the audit risk model by providing effective ways of conducting the required walkthroughs of processes and conducting analytic procedures; 4. process mining allows the auditor to conduct analyses not possible with existing audit tools, such as discovering the ways in which business processes are actually being carried out in practice, and to identify social relationships between individuals. It is our argument that these sources of value have not been fully understood in the process mining literature, which has focused on developing it as a statistical methodology rather than on applying it to audit practice. Only when auditors and audit researchers appreciate what is new and unique about process mining will its acceptance in auditing practice become feasible.

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  • 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.
    • Handle: RePEc:eee:ijoais:v:14:y:2013:i:1:p:1-20
    DOI: 10.1016/j.accinf.2012.06.015
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    References listed on IDEAS

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    1. D. Eric Hirst & Lisa Koonce, 1996. "Audit Analytical Procedures: A Field Investigation," Contemporary Accounting Research, John Wiley & Sons, vol. 13(2), pages 457-486, September.
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    1. 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.
    2. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    3. Koreff, Jared & Weisner, Martin & Sutton, Steve G., 2021. "Data analytics (ab) use in healthcare fraud audits," International Journal of Accounting Information Systems, Elsevier, vol. 42(C).
    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. Kumar, Satish & Marrone, Mauricio & Liu, Qi & Pandey, Nitesh, 2020. "Twenty years of the International Journal of Accounting Information Systems: A bibliometric analysis," International Journal of Accounting Information Systems, Elsevier, vol. 39(C).
    6. Werner, Michael & Wiese, Michael & Maas, Annalouise, 2021. "Embedding process mining into financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).
    7. 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.
    8. Sledgianowski, Deb & Gomaa, Mohamed & Tan, Christine, 2017. "Toward integration of Big Data, technology and information systems competencies into the accounting curriculum," Journal of Accounting Education, Elsevier, vol. 38(C), pages 81-93.
    9. Sudhanshu Singh & Rakesh Verma & Saroj Koul, 2022. "A collaborative method for simultaneous operations: case of an eye clinic," OPSEARCH, Springer;Operational Research Society of India, vol. 59(2), pages 711-731, June.
    10. Krieger, Felix & Drews, Paul & Velte, Patrick, 2021. "Explaining the (non-) adoption of advanced data analytics in auditing: A process theory," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).
    11. 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.
    12. Zerbino, Pierluigi & Stefanini, Alessandro & Aloini, Davide, 2021. "Process Science in Action: A Literature Review on Process Mining in Business Management," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    13. Pall Rikhardsson & Kishore Singh & Peter Best, 2019. "Exploring Continuous Auditing Solutions and Internal Auditing: A Research Note," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 18(4), pages 614-639, December.
    14. Mahama, Habib & Elbashir, Mohamed Z. & Sutton, Steve G. & Arnold, Vicky, 2016. "A further interpretation of the relational agency of information systems: A research note," International Journal of Accounting Information Systems, Elsevier, vol. 20(C), pages 16-25.
    15. Gray, Glen L. & Debreceny, Roger S., 2014. "A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 15(4), pages 357-380.
    16. Sudhanshu Singh & Rakesh Verma & Saroj Koul, 2022. "A data-driven approach to shared decision-making in a healthcare environment," OPSEARCH, Springer;Operational Research Society of India, vol. 59(2), pages 732-746, June.

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