Title: An expert system with neural network and decision tree for predicting audit opinions

Authors: Mehdi Sarikhani; Seyed Mojtaba Saif; Fahime Ebrahimi

Addresses: Department of Accounting, Islamic Azad University, Safashahr Branch, Vahdat Blvd., Safashahr, Postal Code: 7396166561, Iran ' Department of Computer, Islamic Azad University, Safashahr Branch, Vahdat Blvd., Safashahr, Postal Code: 7396166561, Iran ' Department of Accounting, Islamic Azad University, Safashahr Branch, Vahdat Blvd., Safashahr, Postal Code: 7396166561, Iran

Abstract: Nowadays, expert systems being used in various fields have received a great deal of attention. Auditing is one such field, along with determining the audit opinion type. An expert system consists of a knowledge database and an inference engine. The objective of this research is to make an expert system that will be of help to auditors in predicting and determining the different types of audit reports. The expert system receives data or knowledge from financial reports and determines the types of audit opinions by using an artificial neural network and a decision tree as an inference engine. An expert system should be able to explain the solution, but presenting the reason for the results obtained with a neural network is difficult. This study attempts to provide a method that will present simple and understandable reasons for the results obtained with neural networks.

Keywords: audit opinions; expert systems; artificial neural networks; ANNs; decision tree; opinion prediction; auditing; financial reports; inference engine.

DOI: 10.1504/IJCONVC.2014.063745

International Journal of Convergence Computing, 2014 Vol.1 No.2, pp.137 - 148

Received: 11 Jul 2013
Accepted: 17 Sep 2013
Published online: 30 Aug 2014 *

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