IDEAS home Printed from https://ideas.repec.org/a/eee/ijoais/v46y2022ics1467089522000240.html
   My bibliography  Save this article

Explainable Artificial Intelligence (XAI) in auditing

Author

Listed:
  • Zhang, Chanyuan (Abigail)
  • Cho, Soohyun
  • Vasarhelyi, Miklos

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are gaining increasing attention regarding their potential applications in auditing. One major challenge of their adoption in auditing is the lack of explainability of their results. As AI/ML matures, so do techniques that can enhance the interpretability of AI, a.k.a., Explainable Artificial Intelligence (XAI). This paper introduces XAI techniques to auditing practitioners and researchers. We discuss how different XAI techniques can be used to meet the requirements of audit documentation and audit evidence standards. Furthermore, we demonstrate popular XAI techniques, especially Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP), using an auditing task of assessing the risk of material misstatement. This paper contributes to accounting information systems research and practice by introducing XAI techniques to enhance the transparency and interpretability of AI applications applied to auditing tasks.

Suggested Citation

  • Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    • Handle: RePEc:eee:ijoais:v:46:y:2022:i:c:s1467089522000240
    DOI: 10.1016/j.accinf.2022.100572
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1467089522000240
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.accinf.2022.100572?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. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    2. Jeremy Bertomeu & Edwige Cheynel & Eric Floyd & Wenqiang Pan, 2021. "Using machine learning to detect misstatements," Review of Accounting Studies, Springer, vol. 26(2), pages 468-519, June.
    3. Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
    4. Miklós Virág & Tamás Nyitrai, 2014. "Is there a trade-off between the predictive power and the interpretability of bankruptcy models? The case of the first Hungarian bankruptcy prediction model," Acta Oeconomica, Akadémiai Kiadó, Hungary, vol. 64(4), pages 419-440, December.
    5. Margaret H. Christ & Scott A. Emett & Scott L. Summers & David A. Wood, 2021. "Prepare for takeoff: improving asset measurement and audit quality with drone-enabled inventory audit procedures," Review of Accounting Studies, Springer, vol. 26(4), pages 1323-1343, December.
    6. Ralf Ewert & Alfred Wagenhofer, 2019. "Effects of Increasing Enforcement on Financial Reporting Quality and Audit Quality," Journal of Accounting Research, Wiley Blackwell, vol. 57(1), pages 121-168, March.
    7. Beneish, Messod D., 1997. "Detecting GAAP violation: implications for assessing earnings management among firms with extreme financial performance," Journal of Accounting and Public Policy, Elsevier, vol. 16(3), pages 271-309.
    8. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
    9. Ivy Munoko & Helen L. Brown-Liburd & Miklos Vasarhelyi, 2020. "The Ethical Implications of Using Artificial Intelligence in Auditing," Journal of Business Ethics, Springer, vol. 167(2), pages 209-234, November.
    10. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
    11. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 237-291, March.
    12. Kexing Ding & Baruch Lev & Xuan Peng & Ting Sun & Miklos A. Vasarhelyi, 2020. "Machine learning improves accounting estimates: evidence from insurance payments," Review of Accounting Studies, Springer, vol. 25(3), pages 1098-1134, September.
    13. Saikat Sarkar & Matti Tuomala, 2021. "Asset bubbles in explaining top income shares," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(4), pages 707-726, December.
    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. 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.
    2. Julia Brasse & Hanna Rebecca Broder & Maximilian Förster & Mathias Klier & Irina Sigler, 2023. "Explainable artificial intelligence in information systems: A review of the status quo and future research directions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-30, December.
    3. Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023. "A Comprehensive Review on Financial Explainable AI," Papers 2309.11960, arXiv.org.
    4. Diego Valentinetti & Michele A. Reaa, 2023. "Intelligenza artificiale e accounting: le possibili relazioni," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2023(2), pages 93-116.
    5. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
    6. Chen, Xun-Qi & Ma, Chao-Qun & Ren, Yi-Shuai & Lei, Yu-Tian & Huynh, Ngoc Quang Anh & Narayan, Seema, 2023. "Explainable artificial intelligence in finance: A bibliometric review," Finance Research Letters, Elsevier, vol. 56(C).
    7. Sutton, Steve G. & Arnold, Vicky & Holt, Matthew, 2023. "An extension of the theory of technology dominance: Capturing the underlying causal complexity," International Journal of Accounting Information Systems, Elsevier, vol. 50(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. Achakzai, Muhammad Atif Khan & Peng, Juan, 2023. "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, vol. 89(C).
    2. Xiaowei Chen & Cong Zhai, 2023. "Bagging or boosting? Empirical evidence from financial statement fraud detection," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5093-5142, December.
    3. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    4. Lukui Huang & Alan Abrahams & Peter Ractham, 2022. "Enhanced financial fraud detection using cost‐sensitive cascade forest with missing value imputation," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 133-155, July.
    5. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
    6. Dan Amiram & Zahn Bozanic & James D. Cox & Quentin Dupont & Jonathan M. Karpoff & Richard Sloan, 2018. "Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature," Review of Accounting Studies, Springer, vol. 23(2), pages 732-783, June.
    7. Abdullah Albizri & Deniz Appelbaum & Nicholas Rizzotto, 2019. "Evaluation of financial statements fraud detection research: a multi-disciplinary analysis," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 16(4), pages 206-241, December.
    8. Achakzai, Muhammad Atif Khan & Juan, Peng, 2022. "Using machine learning Meta-Classifiers to detect financial frauds," Finance Research Letters, Elsevier, vol. 48(C).
    9. Bolin Liao & Zhendai Huang & Xinwei Cao & Jianfeng Li, 2022. "Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach," Mathematics, MDPI, vol. 10(13), pages 1-14, June.
    10. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    11. Rong Liu & Jujun Huang & Zhongju Zhang, 2023. "Tracking disclosure change trajectories for financial fraud detection," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 584-602, February.
    12. Elias Zavitsanos & Dimitris Mavroeidis & Konstantinos Bougiatiotis & Eirini Spyropoulou & Lefteris Loukas & Georgios Paliouras, 2023. "Financial misstatement detection: a realistic evaluation," Papers 2305.17457, arXiv.org.
    13. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2021. "A Response to "Critique of an Article on Machine Learning in the Detection of Accounting Fraud"," Econ Journal Watch, Econ Journal Watch, vol. 18(1), pages 1-71–78, March.
    14. Miao Liu, 2022. "Assessing Human Information Processing in Lending Decisions: A Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 607-651, May.
    15. Xi Chen & Yang Ha (Tony) Cho & Yiwei Dou & Baruch Lev, 2022. "Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 467-515, May.
    16. Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
    17. Kelton, Andrea Seaton & Murthy, Uday S., 2023. "Reimagining design science and behavioral science AIS research through a business activity lens," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
    18. So-Jin Yu & Jin-Sung Rha, 2021. "Research Trends in Accounting Fraud Using Network Analysis," Sustainability, MDPI, vol. 13(10), pages 1-26, May.
    19. Lars Elend & Sebastian A. Tideman & Kerstin Lopatta & Oliver Kramer, 2020. "Earnings Prediction with Deep Learning," Papers 2006.03132, arXiv.org, revised Oct 2020.
    20. Curtis Nicholls, 2016. "The impact of SEC investigations and accounting and auditing enforcement releases on firms’ cost of equity capital," Review of Quantitative Finance and Accounting, Springer, vol. 47(1), pages 57-82, July.

    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:46:y:2022:i:c:s1467089522000240. 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.