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Bayesian Fraud Risk Formula for Financial Statement Audits

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  • RAJENDRA P. SRIVASTAVA
  • THEODORE J. MOCK
  • JERRY L. TURNER

Abstract

In this article we extend the work of Loebbecke et al. (1989) and illustrate the use of an evidential reasoning approach for developing fraud risk analysis models under the Bayesian framework. New formulations facilitating fraud risk assessments are needed because decision tree approaches previously used to develop analytical models are not appropriate in complex situations involving several interrelated variables. To demonstrate the evidential reasoning approach, a fraud risk assessment formula is derived and illustrated. The fraud risk formula captures the impact of the presence or absence of and interrelationships between the three ‘fraud triangle’ risk factors: Incentives, Attitude and Opportunities. The formula includes the impact of risks and controls related to these three fraud risk factors as well as the impact of forensic audit procedures and relevant analytical and other procedures that provide evidence for the presence or absence of fraud. This formula may be used in audit practice both to help plan the audit and to assess fraud risk sequentially as audit evidence is obtained.

Suggested Citation

  • Rajendra P. Srivastava & Theodore J. Mock & Jerry L. Turner, 2009. "Bayesian Fraud Risk Formula for Financial Statement Audits," Abacus, Accounting Foundation, University of Sydney, vol. 45(1), pages 66-87, March.
    • Handle: RePEc:bla:abacus:v:45:y:2009:i:1:p:66-87
    DOI: 10.1111/j.1467-6281.2009.00278.x
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    References listed on IDEAS

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    1. R. G. Cowell & R. J. Verrall & Y. K. Yoon, 2007. "Modeling Operational Risk With Bayesian Networks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(4), pages 795-827, December.
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    Cited by:

    1. Dennis M. Lopez & Pamela C. Smith, 2010. "Auditor Type and Audit Quality Differences in Nonprofit Healthcare Organizations – U.S. Evidence," Working Papers 0107, College of Business, University of Texas at San Antonio.
    2. Arzhenovskiy S.V. & Bakhteev A.V. & Sinyavskaya T.G. & Hahonova N.N., 2019. "Audit Risk Assessment Model," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(Special 1), pages 74-85.
    3. Muhammad Azizul Islam, 2014. "Bribery and corruption in Australian local councils," Public Money & Management, Taylor & Francis Journals, vol. 34(6), pages 441-446, November.
    4. Caplan, Dennis & Dutta, Saurav K., 2016. "Managing the risk of misleading financial metrics in annual reports: A first step towards providing assurance over management's discussion," Journal of Accounting Literature, Elsevier, vol. 36(C), pages 1-27.
    5. Srivastava, Rajendra P., 2011. "An introduction to evidential reasoning for decision making under uncertainty: Bayesian and belief function perspectives," International Journal of Accounting Information Systems, Elsevier, vol. 12(2), pages 126-135.
    6. Tjiptogoro Dinarjo Soehari & Iffah Budiningsih, 2020. "Model of Bureaucratic Corruption Prevention," International Journal of Asian Social Science, Asian Economic and Social Society, vol. 10(10), pages 638-646, October.
    7. Shaio Yan Huang & Chi-Chen Lin & An-An Chiu & David C. Yen, 0. "Fraud detection using fraud triangle risk factors," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    8. Adi Masli & Matthew G. Sherwood & Rajendra P. Srivastava, 2018. "Attributes and Structure of an Effective Board of Directors: A Theoretical Investigation," Abacus, Accounting Foundation, University of Sydney, vol. 54(4), pages 485-523, December.
    9. Shaio Yan Huang & Chi-Chen Lin & An-An Chiu & David C. Yen, 2017. "Fraud detection using fraud triangle risk factors," Information Systems Frontiers, Springer, vol. 19(6), pages 1343-1356, December.

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