IDEAS home Printed from https://ideas.repec.org/a/ejw/journl/v18y2021i1p61-70.html
   My bibliography  Save this article

Critique of an Article on Machine Learning in the Detection of Accounting Fraud

Author

Listed:
  • Stephen Walker

Abstract

This critique examines the results of an article that applies machine learning to the detection of accounting fraud, published in Journal of Accounting Research. Their key finding is that machine learning improved fraud detection by 70 percent above a previously published logistic regression. The authors make their data and Matlab code available at Github. Using their files, I replicate their study. Upon closer inspection, we see that some fraudulent firms were contained in both the training and test samples, which improves the results of their model, but contradicts what was described in the published paper. I asked the authors about this issue and gratefully received a response. The response is quoted in the present critique. Getting a proper assessment of the potential of machine learning is important, as such techniques and models are relied upon by industry practitioners and regulators, including the Securities and Exchange Commission.

Suggested Citation

  • Stephen Walker, 2021. "Critique of an Article on Machine Learning in the Detection of Accounting Fraud," Econ Journal Watch, Econ Journal Watch, vol. 18(1), pages 1-61–70, March.
    • Handle: RePEc:ejw:journl:v:18:y:2021:i:1:p:61-70
    as

    Download full text from publisher

    File URL: https://econjwatch.org/File+download/1185/WalkerMar2021.pdf?mimetype=pdf
    Download Restriction: no
    File URL: https://econjwatch.org/1231
    Download Restriction: no
    ---><---

    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.
    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. 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.
    2. 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.
    3. Stephen Walker, 2022. "Erroneous Erratum to Accounting Fraud Article," Econ Journal Watch, Econ Journal Watch, vol. 19(2), pages 190–203-1, September.

    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. 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.
    2. Adebayo Oshingbesan & Eniola Ajiboye & Peruth Kamashazi & Timothy Mbaka, 2022. "Model-Free Reinforcement Learning for Asset Allocation," Papers 2209.10458, arXiv.org.
    3. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    4. 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.
    5. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
    6. 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.
    7. Paul Geertsema & Helen Lu, 2023. "Relative Valuation with Machine Learning," Journal of Accounting Research, Wiley Blackwell, vol. 61(1), pages 329-376, March.
    8. Stephen Walker, 2022. "Erroneous Erratum to Accounting Fraud Article," Econ Journal Watch, Econ Journal Watch, vol. 19(2), pages 190–203-1, September.
    9. Jun, So Young & Kim, Dong Sung & Jung, Suk Yoon & Jun, Sang Gyung & Kim, Jong Woo, 2022. "Stock investment strategy combining earnings power index and machine learning," International Journal of Accounting Information Systems, Elsevier, vol. 47(C).
    10. 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).
    11. Achakzai, Muhammad Atif Khan & Juan, Peng, 2022. "Using machine learning Meta-Classifiers to detect financial frauds," Finance Research Letters, Elsevier, vol. 48(C).
    12. Wang, Yichen & Hu, Jun & Chen, Jia, 2023. "Does Fintech facilitate cross-border M&As? Evidence from Chinese A-share listed firms," International Review of Financial Analysis, Elsevier, vol. 85(C).
    13. Elias Zavitsanos & Dimitris Mavroeidis & Konstantinos Bougiatiotis & Eirini Spyropoulou & Lefteris Loukas & Georgios Paliouras, 2023. "Financial misstatement detection: a realistic evaluation," Papers 2305.17457, arXiv.org.
    14. Yasheng Chen & Zhuojun Wu, 2022. "Financial Fraud Detection of Listed Companies in China: A Machine Learning Approach," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    15. 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.
    16. Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022. "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, vol. 48(C).
    17. Dennis W. Campbell & Ruidi Shang, 2022. "Tone at the Bottom: Measuring Corporate Misconduct Risk from the Text of Employee Reviews," Management Science, INFORMS, vol. 68(9), pages 7034-7053, September.
    18. van der Heijden, Hans, 2022. "Predicting industry sectors from financial statements: An illustration of machine learning in accounting research," The British Accounting Review, Elsevier, vol. 54(5).
    19. Wang, Delu & Chen, Fan & Mao, Jinqi & Liu, Nannan & Rong, Fangyu, 2022. "Are the official national data credible? Empirical evidence from statistics quality evaluation of China's coal and its downstream industries," Energy Economics, Elsevier, vol. 114(C).
    20. 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.

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

    Statistics

    Access and download statistics

    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:ejw:journl:v:18:y:2021:i:1:p:61-70. 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: Jason Briggeman (email available below). General contact details of provider: https://edirc.repec.org/data/edgmuus.html .

    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.