Support vector machines, decision trees and neural networks for auditor selection

Abstract

The selection of a proper auditor is driven by several factors. Here, we use three data mining classification techniques to predict the auditor choice. The methods used are Decision Trees, Neural Networks and Support Vector Machines. The developed models are compared in term of their performances. The wrapper feature selection technique is used for the Decision Tree model. Two models reveal that the level of debt is a factor that influences the auditor choice decision. This study has implications for auditors, investors, company decision makers and researchers.