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Detection of Management Fraud: A Neural Network Approach

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  • Kurt Fanning
  • Kenneth O. Cogger
  • Rajendra Srivastava

Abstract

The detection of management fraud is an important issue facing the auditing profession. A major contributor to this issue is the Loebbecke and Willingham (1988) conceptual model for the detection of management fraud. A cascaded Logit approach using the Loebbecke and Willingham model was developed in Bell et al. (1993). The present study offers an alternative approach using Artificial Neural Networks (ANNs). This paper develops a successful discriminator of management fraud using both the generalized adaptive neural network architectures (GANNA) and the Adaptive Logic Network (ALN) approaches to designing neural networks. The discriminant functions can distinguish between fraudulent and non‐fraudulent companies with superior accuracy to the cascaded Logit results of Bell et al. (1993). Finally, the discriminant function provides a parsimonious set of questions useful for detecting management fraud.

Suggested Citation

  • Kurt Fanning & Kenneth O. Cogger & Rajendra Srivastava, 1995. "Detection of Management Fraud: A Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 113-126, June.
    • Handle: RePEc:wly:isacfm:v:4:y:1995:i:2:p:113-126
    DOI: 10.1002/j.1099-1174.1995.tb00084.x
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    References listed on IDEAS

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    Cited by:

    1. Ehsan Habib Feroz & Taek Mu Kwon & Victor S. Pastena & Kyungjoo Park, 2000. "The efficacy of red flags in predicting the SEC's targets: an artificial neural networks approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 145-157, September.
    2. Chrysovalantis Gaganis & Fotios Pasiouras & Charalambos Spathis & Constantin Zopounidis, 2007. "A comparison of nearest neighbours, discriminant and logit models for auditing decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 23-40, January.
    3. Chrysovalantis Gaganis, 2009. "Classification techniques for the identification of falsified financial statements: a comparative analysis," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(3), pages 207-229, July.
    4. Ch. Spathis & M. Doumpos & C. Zopounidis, 2002. "Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques," European Accounting Review, Taylor & Francis Journals, vol. 11(3), pages 509-535.
    5. Kenneth O. Cogger, 2010. "Nonlinear multiple regression methods: a survey and extensions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 17(1), pages 19-39, January.
    6. Sridhar Ramamoorti & Andrew D. Bailey Jr & Richard O. Traver, 1999. "Risk assessment in internal auditing: a neural network approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 8(3), pages 159-180, September.
    7. Arben Asllani & Manjola Naco, 2015. "Using Benford¡¯s Law for Fraud Detection in Accounting Practices," Journal of Social Science Studies, Macrothink Institute, vol. 2(1), pages 129-143, January.
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