Predicting fraudulent financial reporting using artificial neural network
Abstract
Purpose
This paper aims to explore the effectiveness of an artificial neural network (ANN) in predicting fraudulent financial reporting in small market capitalization companies in Malaysia.
Design/methodology/approach
Based on the concepts of ANN, a mathematical model was developed to compare non-fraud and fraud companies selected from among small market capitalization companies in Malaysia; the fraud companies had already been charged by the Securities Commission for falsification of financial statements. Ten financial ratios are used as fraud risk indicators to predict fraudulent financial reporting using ANN.
Findings
The findings indicate that the proposed ANN methodology outperforms other statistical techniques widely used for predicting fraudulent financial reporting.
Originality/value
The study is one of few to adopt the ANN approach for the prediction of financial reporting fraud.
Keywords
Acknowledgements
The research was supported by Accounting Research Institute (ARI) and the Faculty of Accountancy within Universiti Teknologi Mara, Malaysia.
This research was supported by HICoE grant, Ministry of Education, Malaysia.
Citation
Omar, N., Johari, Z.‘. and Smith, M. (2017), "Predicting fraudulent financial reporting using artificial neural network", Journal of Financial Crime, Vol. 24 No. 2, pp. 362-387. https://doi.org/10.1108/JFC-11-2015-0061
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited