An increasing number of studies apply machine learning techniques to corporate fraud detection, a... more An increasing number of studies apply machine learning techniques to corporate fraud detection, and most claim that machine learning is more efficient than traditional methods at this task. The primary purpose of this research is to assess the efficiency of machine learning in detecting accounting fraud. Using a sample of Chinese firms sanctioned for accounting fraud between 2007 and 2022 and by combining powerful machine learning and SAS visualisation techniques, we find mixed results. On the one hand, unlike many recent studies, we fail to generate robust and conclusive evidence that existing machine learning techniques are more efficient than traditional logistic regression in detecting accounting fraud. On the other hand, we find that deep learning offers great potential for fraud detection. Our study contributes to a better understanding of the application of machine learning in accounting research.
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Papers by Wenyan Wu