Critique of an Article on Machine Learning in the Detection of Accounting Fraud
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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.
Response to this article by Yang Bao, Bin Ke, Bin Li, Y. Julia Yu, and Jie Zhang: A Response to “Critique of an Article on Machine Learning in the Detection of Accounting Fraud” (EJW, March 2021).