IDEAS home Printed from https://ideas.repec.org/a/eee/ijoais/v54y2024ics1467089524000319.html
   My bibliography  Save this article

Artificial intelligence co-piloted auditing

Author

Listed:
  • Gu, Hanchi
  • Schreyer, Marco
  • Moffitt, Kevin
  • Vasarhelyi, Miklos

Abstract

This paper proposes the concept of artificial intelligence co-piloted auditing, emphasizing the collaborative potential of auditors and foundation models in the auditing domain. The paper discusses the future relationship and interactions of human auditors and AI, imagining an audit setup where auditors’ capabilities are enhanced through artificial intelligence across a variety of audit tasks. To exemplify the potential of this co-piloted audit paradigm, we illustrate a systematic fine-tuning approach to foundation models using Chain-of-Thought prompting. This study showcases how foundation models can work as collaborators flexibly with auditors, enabling the model to accurately identify transactions from instructions. This study provides a detailed description of the formulated prompt protocols and the corresponding responses generated by ChatGPT, ensuring reproducibility. We envision this work as an initial step towards the widespread implementation of co-piloted auditing, paving the way for more efficient, accurate, and insightful audit procedures.

Suggested Citation

  • Gu, Hanchi & Schreyer, Marco & Moffitt, Kevin & Vasarhelyi, Miklos, 2024. "Artificial intelligence co-piloted auditing," International Journal of Accounting Information Systems, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:ijoais:v:54:y:2024:i:c:s1467089524000319
    DOI: 10.1016/j.accinf.2024.100698
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1467089524000319
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.accinf.2024.100698?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. 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.
    2. Tyna Eloundou & Sam Manning & Pamela Mishkin & Daniel Rock, 2023. "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models," Papers 2303.10130, arXiv.org, revised Aug 2023.
    3. Wood, David A. & Achhpilia, Muskan P. & Adams, Mollie T. & Aghazadeh, Sanaz & Akinyele, Kazeem & Akpan, Mfon & Allee, Kristian D. & Allen, Abigail M. & Almer, Elizabeth D. & Ames, Daniel & Arity, Vikt, 2023. "The ChatGPT artificial intelligence chatbot: How well does it answer accounting assessment questions?," Other publications TiSEM b4a29b3e-18a1-4259-a70c-d, Tilburg University, School of Economics and Management.
    4. Nanyi Fei & Zhiwu Lu & Yizhao Gao & Guoxing Yang & Yuqi Huo & Jingyuan Wen & Haoyu Lu & Ruihua Song & Xin Gao & Tao Xiang & Hao Sun & Ji-Rong Wen, 2022. "Towards artificial general intelligence via a multimodal foundation model," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Stephan Hollander & Maarten Pronk & Erik Roelofsen, 2010. "Does Silence Speak? An Empirical Analysis of Disclosure Choices During Conference Calls," Journal of Accounting Research, Wiley Blackwell, vol. 48(3), pages 531-563, June.
    6. 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.
    7. Shijie Wu & Ozan Irsoy & Steven Lu & Vadim Dabravolski & Mark Dredze & Sebastian Gehrmann & Prabhanjan Kambadur & David Rosenberg & Gideon Mann, 2023. "BloombergGPT: A Large Language Model for Finance," Papers 2303.17564, arXiv.org, revised Dec 2023.
    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. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).
    2. Marc Eulerich & Aida Sanatizadeh & Hamid Vakilzadeh & David A. Wood, 2024. "Is it all hype? ChatGPT’s performance and disruptive potential in the accounting and auditing industries," Review of Accounting Studies, Springer, vol. 29(3), pages 2318-2349, September.
    3. Christoph Riedl & Eric Bogert, 2024. "Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity," Papers 2409.18660, arXiv.org.
    4. Carvajal, Daniel & Franco, Catalina & Isaksson, Siri, 2024. "Will Artificial Intelligence Get in the Way of Achieving Gender Equality?," Discussion Paper Series in Economics 3/2024, Norwegian School of Economics, Department of Economics, revised 20 Mar 2025.
    5. Evangelos Katsamakas & Oleg V. Pavlov & Ryan Saklad, 2024. "Artificial intelligence and the transformation of higher education institutions," Papers 2402.08143, arXiv.org.
    6. Lezhi Li & Ting-Yu Chang & Hai Wang, 2023. "Multimodal Gen-AI for Fundamental Investment Research," Papers 2401.06164, arXiv.org.
    7. Draca, Mirko & Nathan, Max & Nguyen-Tien, Viet & Oliveira-Cunha, Juliana & Rosso, Anna & Valero, Anna, 2024. "The New Wave? The Role of Human Capital and STEM Skills in Technology Adoption in the UK," The Warwick Economics Research Paper Series (TWERPS) 1521, University of Warwick, Department of Economics.
    8. Ballantine, Joan & Boyce, Gordon & Stoner, Greg, 2024. "A critical review of AI in accounting education: Threat and opportunity," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 99(C).
    9. Chen, Qi & Vashishtha, Rahul, 2017. "The effects of bank mergers on corporate information disclosure," Journal of Accounting and Economics, Elsevier, vol. 64(1), pages 56-77.
    10. Berlinski, Elise & Morales, Jérémy & Sponem, Samuel, 2024. "Artificial imaginaries: Generative AIs as an advanced form of capitalism," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 99(C).
    11. Josef Schroth, 2018. "Managerial Compensation and Stock Price Manipulation," Journal of Accounting Research, Wiley Blackwell, vol. 56(5), pages 1335-1381, December.
    12. Caleb Peppiatt, 2024. "The Future of Work: Inequality, Artificial Intelligence, and What Can Be Done About It. A Literature Review," Papers 2408.13300, arXiv.org.
    13. D'Al, Francesco & Santarelli, Enrico & Vivarelli, Marco, 2024. "The KSTE+I approach and the advent of AI technologies: evidence from the European regions," GLO Discussion Paper Series 1473, Global Labor Organization (GLO).
    14. Amali Matharaarachchi & Wishmitha Mendis & Kanishka Randunu & Daswin De Silva & Gihan Gamage & Harsha Moraliyage & Nishan Mills & Andrew Jennings, 2024. "Optimizing Generative AI Chatbots for Net-Zero Emissions Energy Internet-of-Things Infrastructure," Energies, MDPI, vol. 17(8), pages 1-19, April.
    15. Anna Davies & Betsy Donald & Mia Gray, 2023. "The power of platforms—precarity and place," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 16(2), pages 245-256.
    16. Canace, Thomas G. & Cianci, Anna M. & (Kelvin) Liu, Xiaotao & Tsakumis, George T., 2020. "Paid for looks when others are looking: CEO facial traits, compensation, and corporate visibility," Journal of Business Research, Elsevier, vol. 115(C), pages 85-100.
    17. Tarek A Hassan & Stephan Hollander & Laurence van Lent & Ahmed Tahoun, 2019. "Firm-Level Political Risk: Measurement and Effects," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(4), pages 2135-2202.
    18. Mark E. Lokanan & Prerna Sharma, 2023. "Two Decades of Accounting Fraud Research: The Missing Meso-Level Analysis," SAGE Open, , vol. 13(3), pages 21582440231, September.
    19. Hoyoung Lee & Youngsoo Choi & Yuhee Kwon, 2024. "Quantifying Qualitative Insights: Leveraging LLMs to Market Predict," Papers 2411.08404, arXiv.org.
    20. Michaely, Roni & Rubin, Amir & Vedrashko, Alexander, 2016. "Further evidence on the strategic timing of earnings news: Joint analysis of weekdays and times of day," Journal of Accounting and Economics, Elsevier, vol. 62(1), pages 24-45.

    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:54:y:2024:i:c:s1467089524000319. 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.