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

Stephen Walker

Econ Journal Watch2021article
AJG 2ABDC B
Weight
0.51

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.

13 citations

Cite this paper

@article{stephen2021,
  title        = {{Critique of an Article on Machine Learning in the Detection of Accounting Fraud}},
  author       = {Stephen Walker},
  journal      = {Econ Journal Watch},
  year         = {2021},
}

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

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Evidence weight

0.51

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.42 × 0.4 = 0.17
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.