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Machine Learning Algorithms and Auditor’s Assessments of the Risks Material Misstatement: Evidence from the Restatement of Listed London Companies |
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PP: 1285-1298 |
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doi:10.18576/isl/120443
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Author(s) |
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H. G. Aly,
O. R. Elguoshy,
M. Z. Metwaly,
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Abstract |
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| The purpose of this study is to investigate the relationship between machine learning algorithms and auditors assessments of the risks of material misstatement and restatement. Additionally, a focus on the effect of machine learning algorithms (SVM, Naïve Bayes, and K-means) on misstatement and restatement in London companies. The final sample of the study is 304 firm year observations. Which covers the listed firms on the London Stock Exchange and the period from 2018 to 2020. Especially, the firms that restated their financial statements -even just once- during the study period. The results showed a positive significant effect of machine learning techniques (K-means, Naïve Bayes, and SVM) on the intentional misstatements, which means that using machine learning techniques helps in determining the intentional misstatements. The results also showed a negative significant effect of the same techniques (K-means, Naïve Bayes, and SVM) on the restatements, which means that using machine learning techniques helps in avoiding the restatements.
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