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

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  • 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
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    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.
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    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.

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    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

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