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Neural network models: Foundations and applications to an audit decision problem

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  • Rebecca Wu

Abstract

We investigate the possibility of applying artificial intelligence to solve an audit decision problem faced by the public sector (namely, the tax auditor of the Internal Revenue Services) when targeting firms for further investigation. We propose that the neural network will overcome problems faced by a direct knowledge acquisition method in building an expert system to preserve the expertise of senior auditors of the IRS in Taiwan. An explanation of the neural network theory is provided with regard to multi- and single-layered neural networks. Statistics reveal that the neural network performs favorably, and that three-layer networks perform better than two-layer neural networks. The results strongly suggest that neural networks can be used to identify firms requiring further auditing investigation, and also suggest future implications for intelligent auditing machines. Copyright Kluwer Academic Publishers 1997

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  • Rebecca Wu, 1997. "Neural network models: Foundations and applications to an audit decision problem," Annals of Operations Research, Springer, vol. 75(0), pages 291-301, January.
    • Handle: RePEc:spr:annopr:v:75:y:1997:i:0:p:291-301:10.1023/a:1018915714606
    DOI: 10.1023/A:1018915714606
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    Cited by:

    1. León, Carlos & Barucca, Paolo & Acero, Oscar & Gage, Gerardo & Ortega, Fabio, 2020. "Pattern recognition of financial institutions’ payment behavior," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    2. Carlos León & José Fernando Moreno & Jorge Cely, 2016. "Whose Balance Sheet is this? Neural Networks for Banks’ Pattern Recognition," Borradores de Economia 959, Banco de la Republica de Colombia.
    3. T. Slavici & S. Maris & M. Pirtea, 2016. "Usage of artificial neural networks for optimal bankruptcy forecasting. Case study: Eastern European small manufacturing enterprises," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 385-398, January.
    4. Dieudonné Tchuente & Serge Nyawa, 2022. "Real estate price estimation in French cities using geocoding and machine learning," Annals of Operations Research, Springer, vol. 308(1), pages 571-608, January.
    5. Olalere Isaac Opeyemi, 2022. "Predicting Trade Mispricing: A Gaussian Multivariate Anomaly Detection Model ," GATR Journals jber221, Global Academy of Training and Research (GATR) Enterprise.

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