Machine Learning Algorithms and Auditor’s Assessments of the Risks Material Misstatement - Evidence from the Restatement of Listed London Companies

Aug 2, 2026

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Study examines the relationship between machine learning algorithms and auditors’ assessments of the risks of material misstatement, with an additional focus on how specific techniques influence both misstatement and restatement in London listed companies. The analysis uses 304 firm-year observations covering 2018–2020, emphasizing firms that restated financial statements at least once during the period. Findings indicate significant effects of SVM, Naïve Bayes, and K-means on intentional misstatements and restatements, supporting the use of machine learning to improve identification and reduce restatement.

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Information Sciences Letters | Volume 12
Issue 4 Apr. 2023 | Article 43 | | --- | --- | | 2023
Machine Learning Algorithms and Auditor’s Assessments of the Risks Material Misstatement: Evidence from the Restatement of Listed London Companies
H. G. Aly
Department of Accounting & Auditing, Faculty of Commerce, Suez Canal University, Egypt, [dr.mostafazaki@yahoo.com](dr.mostafazaki@yahoo.com)
O. R. Elguoshy
Department of Accounting & Auditing, Higher Institute of Computer and Business Administration, Egypt, [dr.mostafazaki@yahoo.com](dr.mostafazaki@yahoo.com)
M. Z. Metwaly
Department of Accounting & Auditing, Faculty of Commerce, Suez Canal University, Egypt, [dr.mostafazaki@yahoo.com](dr.mostafazaki@yahoo.com)
Follow this and additional works at: [https://digitalcommons.aaru.edu.jo/isl](https://digitalcommons.aaru.edu.jo/isl) | | Recommended Citation G. Aly, H.; R. Elguoshy, O.; and Z. Metwaly, M. (2023) "Machine Learning Algorithms and Auditor’s Assessments of the Risks Material Misstatement: Evidence from the Restatement of Listed London Companies," Information Sciences Letters: Vol. 12 : Iss. 4 , PP-. Available at: [https://digitalcommons.aaru.edu.jo/isl/vol12/iss4/43](https://digitalcommons.aaru.edu.jo/isl/vol12/iss4/43) This Article is brought to you for free and open access by Arab Journals Platform. It has been accepted for inclusion in Information Sciences Letters by an authorized editor. The journal is hosted on Digital Commons, an Elsevier platform. For more information, please contact [rakan@aaru.edu.jo](rakan@aaru.edu.jo), [marah@aaru.edu.jo](marah@aaru.edu.jo), [u.murad@aaru.edu.jo](u.murad@aaru.edu.jo). Information Sciences Letters An International Journal [http://dx.doi.org/10.18576/isl/120443](http://dx.doi.org/10.18576/isl/120443) Machine Learning Algorithms and Auditor’s Assessments of the Risks Material Misstatement: Evidence from the Restatement of Listed London Companies H. G. Aly 1, O. R. Elguoshy 2 and M. Z. Metwaly 1,* 1Department of Accounting & Auditing, Faculty of Commerce, Suez Canal University, Egypt 2Department of Accounting & Auditing, Higher Institute of Computer and Business Administration, Egypt Received: 2 Feb. 2023, Revised: 12 Mar. 2023, Accepted: 21 Mar. 2023. Published online: 1 Apr. 2023. Abstract: 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. Keywords: Machine Learning, Misstatement, Restatement, SVM, Naïve Bayes, K-means. 1. Introduction The general objective of autonomous machine intelligence is artificial intelligence (AI), and the specific scientific method for reaching AI in this sense is machine learning [1] . All machine learning is included in artificial intelligence, but not all AI is machine learning. One example of artificially intelligent software that excludes machine learning is an algorithm.[2] Machine learning is the process of analyzing data using algorithms, finding implicit patterns, and applying the patterns found t

FAQs

What is the study’s main purpose?

To investigate the relationship between machine learning algorithms and auditors’ assessments of the risks of material misstatement, and to examine how machine learning techniques affect misstatement and restatement in London companies.

Which machine learning algorithms are analyzed in the study?

The study focuses on SVM, Naïve Bayes, and K-means and evaluates their effects on intentional misstatements and restatements.

What sample and period does the research use?

The sample includes 304 firm-year observations from London Stock Exchange listed firms covering 2018 to 2020, with particular attention to firms that restated their financial statements during the period.

Aug 2, 2026

Upload by Finn