Identifying qualified audit opinions by artificial neural networks

Data mining methods can be used in order to facilitate auditors to issue their opinion. This paper for the first time in Iran, applies four data mining classification techniques to develop models capable of identifying auditor’s opinion. Four type of techniques were utilized in this study including: Multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), radial basic functions network (RBF), and logistic regression (LR). Input vector included a qualitative variable as well as several quantitative variables. Our results proved the high capability of MLP neural network in identifying different types of auditor's opinion. PNN was the most balanced model in identifying type of auditor's opinion, and had closet amount of error in identifying unqualified (clean) and qualified type of reports, as compared to other models. RBF neural network in comparison with other models is of the highest performance in identifying qualified type of opinion and LR has the poorest performance in identifying qualified opinion. The results of this study can be useful to internal and external auditors and companies decision-makers.   Key words: Qualified auditors’ opinions, auditing, multi-layer perceptron neural network, probabilistic neural network, radial basic functions.