IDEAS home Printed from https://ideas.repec.org/a/iif/iifjrn/v23y2008i273p119-132.html
   My bibliography  Save this article

Control risk assessment in auditing: Artificial neural network approach

Author

Listed:
  • F.Münevver YILANCI

    (Osmangazi Üniversitesi)

  • Birol YILDIZ

    (Osmangazi Üniversitesi)

Abstract

The purpose of the study is to develop a decision support tool with Artificial Neural Network for the use of external auditors in assessing preliminary control risk level. A survey was conducted to collect internal control data of top 500 industrial corporations in Turkey. The number of surveys returned was 169 out of 500. An external auditor assessed control risk level for each corporation. While the research used 100 firms to develop an artificial neural network model, the remaining 69 firms were selected for testing the model. The findings indicated that the artificial neural network model could mimic the auditor’s decision. The category accuracy rate between the predictions of the artificial neural network model and the auditor opinion was 71.01%. In conclusion, the artificial neural network model provided a useful decision support tool for auditors to assess preliminary control risk level.

Suggested Citation

  • F.Münevver YILANCI & Birol YILDIZ, 2008. "Control risk assessment in auditing: Artificial neural network approach," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 23(273), pages 119-132.
    • Handle: RePEc:iif:iifjrn:v:23:y:2008:i:273:p:119-132
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Keywords

    Auditing; Internal Control; Risk Assessment; Artificial Neural Networks;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing

    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:iif:iifjrn:v:23:y:2008:i:273:p:119-132. 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: Ali Bilge (email available below). General contact details of provider: http://iif.com.tr .

    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.