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Predicting fraudulent financial reporting using artificial neural network

Author

Listed:
  • Normah Omar
  • Zulaikha ‘Amirah Johari
  • Malcolm Smith

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.

Suggested Citation

  • Normah Omar & Zulaikha ‘Amirah Johari & Malcolm Smith, 2017. "Predicting fraudulent financial reporting using artificial neural network," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 24(2), pages 362-387, May.
    • Handle: RePEc:eme:jfcpps:jfc-11-2015-0061
    DOI: 10.1108/JFC-11-2015-0061
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    Citations

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    Cited by:

    1. Hussein Ali Mroueh, 2024. "The Role of Financial Audit in the Corporate Governance Process: An In-depth Analysis," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 36-42.
    2. Maria Tragouda & Michalis Doumpos & Constantin Zopounidis, 2024. "Identification of fraudulent financial statements through a multi‐label classification approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.

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