IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i24p17008-d1007703.html
   My bibliography  Save this article

A Full Population Auditing Method Based on Machine Learning

Author

Listed:
  • Yasheng Chen

    (Department of Accounting, School of Management, Xiamen University, Xiamen 361005, China)

  • Zhuojun Wu

    (Department of Accounting, School of Management, Xiamen University, Xiamen 361005, China)

  • Hui Yan

    (Department of Accounting, School of Management, Xiamen University, Xiamen 361005, China)

Abstract

As it is urgent to change the traditional audit sampling method that is based on manpower to meet the growing audit demand in the era of big data. This study uses empirical methods to propose a full population auditing method based on machine learning. This method can extend the application scope of the audit to all samples through the self-learning feature of machine learning, which helps to address the dependence on auditors’ personal experience and the audit risks arising from audit sampling. First, this paper demonstrates the feasibility of this method, then selects the financial data of a large enterprise for full population testing, and finally summarizes the critical steps of practical applications. The study results indicate that machine learning for full population auditing is able to detect, in all samples, abnormal business whose execution does not adhere to existing accounting rules, as well as abnormal business with irregular accounting rules, thus improving the efficiency of internal control audits. By combining the learning ability of machine-learning algorithms and the arithmetic power of computers, the proposed full population auditing method provides a feasible approach for the intellectual development of future auditing at the application level.

Suggested Citation

  • Yasheng Chen & Zhuojun Wu & Hui Yan, 2022. "A Full Population Auditing Method Based on Machine Learning," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:17008-:d:1007703
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/24/17008/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/24/17008/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huang, Feiqi & Vasarhelyi, Miklos A., 2019. "Applying robotic process automation (RPA) in auditing: A framework," International Journal of Accounting Information Systems, Elsevier, vol. 35(C).
    2. repec:eme:jal000:j.acclit.2018.01.001 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    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. Bavaresco, Rodrigo Simon & Nesi, Luan Carlos & Victória Barbosa, Jorge Luis & Antunes, Rodolfo Stoffel & da Rosa Righi, Rodrigo & da Costa, Cristiano André & Vanzin, Mariangela & Dornelles, Daniel & J, 2023. "Machine learning-based automation of accounting services: An exploratory case study," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    2. SIPOS Csanád & MÁTÉ Domicián, 2020. "Industrial Environment Selection By Sourcing Strategy In The Case Of North African Countries," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 395-404, July.
    3. Emilio Abad-Segura & Mariana-Daniela González-Zamar, 2020. "Research Analysis on Emerging Technologies in Corporate Accounting," Mathematics, MDPI, vol. 8(9), pages 1-29, September.
    4. Haochen Guo & Petr Polak, 2023. "Intelligent finance and change management implications," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-8, December.
    5. Syaiful Anwar Mohamed & Moamin A. Mahmoud & Mohammed Najah Mahdi & Salama A. Mostafa, 2022. "Improving Efficiency and Effectiveness of Robotic Process Automation in Human Resource Management," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    6. Moisescu Florentina & Moisei Madalina, 2021. "The Future of the Accounting Profession Under the Incidence of Automation," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 276-286.
    7. Costa Diogo António da Silva & Mamede Henrique São & Mira da Silva Miguel, 2022. "Robotic Process Automation (RPA) Adoption: A Systematic Literature Review," Engineering Management in Production and Services, Sciendo, vol. 14(2), pages 1-12, June.
    8. Laurence Viale & Dorsaf Zouari, 2020. "Impact of digitalization on procurement: the case of robotic process automation," Post-Print hal-03695535, HAL.
    9. Roman Šperka & Michal Halaška, 2023. "The performance assessment framework (PPAFR) for RPA implementation in a loan application process using process mining," Information Systems and e-Business Management, Springer, vol. 21(2), pages 277-321, June.
    10. P. V. Thayyib & Rajesh Mamilla & Mohsin Khan & Humaira Fatima & Mohd Asim & Imran Anwar & M. K. Shamsudheen & Mohd Asif Khan, 2023. "State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary," Sustainability, MDPI, vol. 15(5), pages 1-38, February.
    11. Yasheng Chen & Xian Huang & Zhuojun Wu, 2023. "From natural language to accounting entries using a natural language processing method," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 3781-3795, December.
    12. Joshua Onome Imoniana & Daniel Carlos Nava Filho & Edgard Bruno Cornacchione & Luciane Reginato & Cristiane Benetti, 2023. "Impact of Technological Advancements on Auditing of Financial Statements," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 131-159.
    13. 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).
    14. Anassaya Chawviang & Supaporn Kiattisin & Montree Thirasakthana & Theeraya Mayakul, 2023. "A Smart Co-Operative Management Framework Based on an EA Concept for Sustainable Development," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    15. Perdana, Arif & Lee, W. Eric & Mui Kim, Chu, 2023. "Prototyping and implementing Robotic Process Automation in accounting firms: Benefits, challenges and opportunities to audit automation," International Journal of Accounting Information Systems, Elsevier, vol. 51(C).
    16. 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).
    17. Lukas-Valentin Herm & Christian Janiesch & Alexander Helm & Florian Imgrund & Adrian Hofmann & Axel Winkelmann, 2023. "A framework for implementing robotic process automation projects," Information Systems and e-Business Management, Springer, vol. 21(1), pages 1-35, March.

    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:gam:jsusta:v:14:y:2022:i:24:p:17008-:d:1007703. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.