An expert system with neural network and decision tree for predicting audit opinions. (1st January 2014)

• Record Type: Journal Article
• Authors: Sarikhani, Mehdi; Saif, Seyed Mojtaba; Ebrahimi, Fahime
• Is Part Of: International journal of convergence computing. Volume 1: Number 2(2014)
• Page Start: 137
• Page End: 148
• Publication Date: 2014-01-01
• Subjects: audit opinions -- expert system -- artificial neural networks -- ANNs -- decision tree; Computer science -- Periodicals; 004.05
• Languages: English
• ISSNs: 2048-9129

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.