Abstract
For auditors, a key concern is the reliability and quality of the auditing opinion’s final decision. The emerging topic of machine learning in auditing is being increasingly explored by developing trusted and efficient algorithms for classifying auditing opinions. The prediction of audit opinions of Egyptian listed companies is crucial to the security market risk mitigation process. Numerous innovations might be put into practice to raise audit efficiency, improve audit quality, and enhance auditor insight by using machine learning techniques. The aim of this paper is to provide a new audit opinion prediction model for financial statements. To this end, a sample of a group of listed Egyptian companies was selected. The model was trained with the aid of auditor opinion labels using two widely used supervised machine learning classifiers (SVM—Support Vector Machine and NV—Naive Bayes). The obtained results were then compared with the trained model that uses the clustering outcomes as a new relative auditor opinion. The results show that the developed method managed to predict the audit opinion with accuracy rates of 83.7 and 83.9 %, respectively. The performance evaluated in terms of overall prediction accuracy, and the Type I and Type II error rates show that the SVM models have higher results than the Naïve Bayes models. This study indicates that traditional methods have a poor performance using two traditional techniques (logistic and probit regressions).
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Elbrashy, A.M., Abdulaziz, A.M.N., Ibraheem, M.R. (2023). Using Machine Learning Techniques in Predicting Auditor Opinion: Empirical Study. In: Magdi, D., El-Fetouh, A.A., Mamdouh, M., Joshi, A. (eds) Green Sustainability: Towards Innovative Digital Transformation. ITAF 2023. Lecture Notes in Networks and Systems, vol 753. Springer, Singapore. https://doi.org/10.1007/978-981-99-4764-5_15
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