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Neural network detection of management fraud using published financial data

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  • Kurt M. Fanning
  • Kenneth O. Cogger

Abstract

This paper uses a Artificial Neural Network (AutoNet) to develop a model for detecting management fraud. The study offers an in‐depth examination of important publicly available predictors of fraudulent financial statements. We find a model with a high probability of detecting fraudulent financial statements on one sample. The study reinforces the validity and efficiency of AutoNet as a research tool and provides additional empirical evidence regarding the merits of suggested red flags for fraudulent financial statements. © 1998 John Wiley & Sons, Ltd.

Suggested Citation

  • Kurt M. Fanning & Kenneth O. Cogger, 1998. "Neural network detection of management fraud using published financial data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(1), pages 21-41, March.
    • Handle: RePEc:wly:isacfm:v:7:y:1998:i:1:p:21-41
    DOI: 10.1002/(SICI)1099-1174(199803)7:13.0.CO;2-K
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    Cited by:

    1. Bethany Hoogs & Thomas Kiehl & Christina Lacomb & Deniz Senturk, 2007. "A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 41-56, January.
    2. Roshayani Arshad & Sharinah Mohamed Iqbal & Normah Omar, 2015. "Prediction of Business Failure and Fraudulent Financial Reporting: Evidence from Malaysia," Indian Journal of Corporate Governance, , vol. 8(1), pages 34-53, June.
    3. 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.
    4. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
    5. Daniel E. O'Leary, 2010. "Intelligent Systems in Accounting, Finance and Management: ISI journal and proceeding citations, and research issues from most‐cited papers," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 17(1), pages 41-58, January.
    6. Efstathios Kirkos & Charalambos Spathis & Yannis Manolopoulos, 2010. "Audit‐firm group appointment: an artificial intelligence approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 17(1), pages 1-17, January.
    7. Nawaf Almaskati, 2022. "Machine learning in finance: Major applications, issues, metrics, and future trends," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-32, September.
    8. Adrian Gepp & Kuldeep Kumar & Sukanto Bhattacharya, 2021. "Lifting the numbers game: identifying key input variables and a best‐performing model to detect financial statement fraud," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4601-4638, September.
    9. Joanna Wyrobek & Lukasz Poplawski & Marcin Surowka, 2020. "Identification of a Fraudulent Organizational Culture in Enterprises Listed in Warsaw Stock Exchange," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 622-637.
    10. Daniel E. O'Leary, 2009. "Downloads and citations in Intelligent Systems in Accounting, Finance and Management," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 21-31, January.
    11. 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.
    12. Papík, Mário & Papíková, Lenka, 2022. "Detecting accounting fraud in companies reporting under US GAAP through data mining," International Journal of Accounting Information Systems, Elsevier, vol. 45(C).
    13. Marko Špiler & Tijana Matejić & Snežana Knežević & Marko Milašinović & Aleksandra Mitrović & Vesna Bogojević Arsić & Tijana Obradović & Dragoljub Simonović & Vukašin Despotović & Stefan Milojević & Mi, 2022. "Assessment of the Bankruptcy Risk in the Hotel Industry as a Condition of the COVID-19 Crisis Using Time-Delay Neural Networks," Sustainability, MDPI, vol. 15(1), pages 1-54, December.

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