The University of Jordan :: Research Groups :: Financial Statement Audit Utilising Naive...

Initial big data analytics (BDA) implementation regarding data mining mechanisms in the audit process, remain un-der-researched and hence, more investigation is needed in developing classification prediction models utilising data mining analytics to identify appropriate auditing opinion. Empirical results show that the decision tree technique provides a balanced model for choosing the right audit opinion, with it achieving higher evaluation rates for two datasets (for the years 2018 and 2019) than Naive Bayes Networks (NBN), Linear Discriminant Analysis (LDA), and Logistic Regression (LR). In addition, for both datasets the LR model achieved higher evaluation rates than NBN and LDA. Moreover, the evaluation results for NBN exhibited a gap between 2019 and 2018 datasets, which means that it is an unbalanced model and hence, is not a good predictor of audit opinion. This paper is a pioneering effort utilising the sample of a company that have big data, based on financial and non-financial data to develop a model able to predict the correct audit opinion.

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Financial Statement Audit Utilising Naive Bayes Networks, Decision Trees, Linear Discriminant Analysis and Logistic Regression