An Expert System with Neural Network and Decision Tree for Predicting Audit Opinions

Seyed Mojtaba Saif, Mehdi Sarikhani, Fahime Ebrahimi


Nowadays expert system, being used in various fields has 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 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.


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