Supporting the Auditor's Professional Judgment by Using Data Mining Techniques in Predicting Material Misstatements in Financial Statements

Nidal O. Zalloum, Haytham Mamdouh Al-Abbadi, Firas N. Hashem

Abstract


This study aimed to test the ability of data mining techniques in supporting the auditor's opinion for the existence of material misstatements for the financial statements items. The methodology of the study has depended on comparing the neural networks, logistic regression results, and the auditor's opinion. The analytical approach has been used in order to test the hypotheses. The effect of neural network and logistic regression techniques has been studied to test their ability in supporting the auditor's opinion for Jordanian industrial and services sectors. The sample of the study consists of (130) industrial and service companies for the period 2008-2011. The study found that the data mining techniques (logistic regression, neural networks) support the auditor's opinion for the existence of material misstatements in financial statements. In addition, the study discovered that logistic regression technique in supporting auditor's opinion is better than neural networks.

Keywords


Date Mining Techniques, Auditor's Opinion, Neural Networks, Logistic Regression.

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