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Predicting fraudulent financial reporting using artificial neural network

Normah Omar (Accounting Research Institute, Universiti Teknologi MARA, Shah Alam, Malaysia)
Zulaikha ‘Amirah Johari (Accounting Research Institute, Universiti Teknologi MARA, Shah Alam, Malaysia)
Malcolm Smith (College of Business, University of Derby, Derby, UK)

Journal of Financial Crime

ISSN: 1359-0790

Article publication date: 2 May 2017

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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