IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v75y1997i0p291-30110.1023-a1018915714606.html
   My bibliography  Save this article

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

Author

Listed:
  • 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

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1018915714606
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1018915714606?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:75:y:1997:i:0:p:291-301:10.1023/a:1018915714606. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.