IDEAS home Printed from https://ideas.repec.org/a/eee/ijoais/v25y2017icp57-80.html
   My bibliography  Save this article

Financial process mining - Accounting data structure dependent control flow inference

Author

Listed:
  • Werner, Michael

Abstract

The increasing integration of computer technology for the processing of business transactions and the growing amount of financially relevant data in organizations create new challenges for external auditors. The availability of digital data opens up new opportunities for innovative audit procedures. Process mining can be used as a novel data analysis technique to support auditors in this context. Process mining algorithms produce process models by analyzing recorded event logs. Contemporary general purpose mining algorithms commonly use the temporal order of recorded events for determining the control flow in mined process models. The presented research shows how data dependencies related to the accounting structure of recorded events can be used as an alternative to the temporal order of events for discovering the control flow. The generated models provide accurate information on the control flow from an accounting perspective and show a lower complexity compared to those generated using timestamp dependencies. The presented research follows a design science research approach and uses three different real world data sets for evaluation purposes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ijoais:v:25:y:2017:i:c:p:57-80
    DOI: 10.1016/j.accinf.2017.03.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1467089516300264
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.accinf.2017.03.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jans, Mieke & Lybaert, Nadine & Vanhoof, Koen, 2010. "Internal fraud risk reduction: Results of a data mining case study," International Journal of Accounting Information Systems, Elsevier, vol. 11(1), pages 17-41.
    2. 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.
    3. Debreceny, Roger S. & Gray, Glen L., 2010. "Data mining journal entries for fraud detection: An exploratory study," International Journal of Accounting Information Systems, Elsevier, vol. 11(3), pages 157-181.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. 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. Adrian-Cosmin Caraiman, 2020. "Internal Control in Corporate Governance," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 606-611, December.
    4. Werner, Michael & Wiese, Michael & Maas, Annalouise, 2021. "Embedding process mining into financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    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. Marco Schreyer & Timur Sattarov & Christian Schulze & Bernd Reimer & Damian Borth, 2019. "Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks," Papers 1908.00734, arXiv.org.
    4. 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).
    5. 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.
    6. 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.
    7. Chen, Yuh-Jen & Wu, Chun-Han & Chen, Yuh-Min & Li, Hsin-Ying & Chen, Huei-Kuen, 2017. "Enhancement of fraud detection for narratives in annual reports," International Journal of Accounting Information Systems, Elsevier, vol. 26(C), pages 32-45.
    8. Bradford, Marianne & Earp, Julia B. & Grabski, Severin, 2014. "Centralized end-to-end identity and access management and ERP systems: A multi-case analysis using the Technology Organization Environment framework," International Journal of Accounting Information Systems, Elsevier, vol. 15(2), pages 149-165.
    9. Ricardo Sartori Cella & Ercilio Zanolla, 2018. "Benford’s Law and transparency: an analysis of municipal expenditure," Brazilian Business Review, Fucape Business School, vol. 15(4), pages 331-347, July.
    10. Montag, Josef, 2017. "Identifying odometer fraud in used car market data," Transport Policy, Elsevier, vol. 60(C), pages 10-23.
    11. 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.
    12. Montag, Josef, 2015. "Identifying Odometer Fraud: Evidence from the Used Car Market in the Czech Republic," MPRA Paper 65182, University Library of Munich, Germany.
    13. Alles, Michael & Gray, Glen L., 2016. "Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors," International Journal of Accounting Information Systems, Elsevier, vol. 22(C), pages 44-59.
    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. 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.
    16. Kishore Singh & Peter Best, 2020. "Implementing Benford’s Law in Continuous Monitoring Applications," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 19(2), pages 379-404, June.
    17. Werner, Michael & Wiese, Michael & Maas, Annalouise, 2021. "Embedding process mining into financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).
    18. Fay, Rebecca & Negangard, Eric M., 2017. "Manual journal entry testing: Data analytics and the risk of fraud," Journal of Accounting Education, Elsevier, vol. 38(C), pages 37-49.
    19. de Araújo Silva, Archibald & Aparecida Gouvêa, Maria, 2023. "Study on the effect of sample size on type I error, in the first, second and first-two digits excessmad tests," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ijoais:v:25:y:2017:i:c:p:57-80. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-accounting-information-systems/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.