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A fuzzy neural network for assessing the risk of fraudulent financial reporting

Author

Listed:
  • Jerry W. Lin
  • Mark I. Hwang
  • Jack D. Becker

Abstract

Keywords: Fraud, Financial reporting, Risk assessment, Neural nets, Fuzzy logic, Decision‐support systems

Suggested Citation

  • Jerry W. Lin & Mark I. Hwang & Jack D. Becker, 2003. "A fuzzy neural network for assessing the risk of fraudulent financial reporting," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 18(8), pages 657-665, November.
    • Handle: RePEc:eme:majpps:02686900310495151
    DOI: 10.1108/02686900310495151
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    Citations

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    Cited by:

    1. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
    2. Wilson Tsakane Mongwe & Rendani Mbuvha & Tshilidzi Marwala, 2021. "Bayesian inference of local government audit outcomes," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-19, December.
    3. Rong Liu & Jujun Huang & Zhongju Zhang, 2023. "Tracking disclosure change trajectories for financial fraud detection," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 584-602, February.
    4. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    5. Sonika Gupta & Sushil Kumar Mehta, 2024. "Feature Selection for Dimension Reduction of Financial Data for Detection of Financial Statement Frauds in Context to Indian Companies," Global Business Review, International Management Institute, vol. 25(2), pages 323-348, April.

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