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    Audit risk assesment framework using belief function model for Kenya national audit office (KENAO)

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    Date
    2010
    Author
    Gichure, Francis
    Type
    Thesis
    Language
    en
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    Abstract
    Program planning regarding the nature, extent, and timing of procedures is critical to audit efficiency and effectiveness. The prevailing paradigm in practice to accomplish this task is the Audit Risk Model. This model has been found to be deficient and outdated because it fails to incorporate the structure of audit evidence and also fails to represent uncertainties in audit evidence. This article relates belief functions to the structure of audit risk and provides formulas for audit risk under certain simplifying assumptions. These formulas give plausibilities of error in the belief-function sense. The belief-function plausibility represents auditors' intuitive understanding of audit risk better than ordinary probability. The plausibility of a statement, within belief-function theory, measures the extent to which we lack evidence against the statement. High plausibility for error indicates only a lack of assurance, not positive evidence that there is error. This high plausibility does not necessarily indicate any evidence that the statement is materially misstated, and hence it is inappropriate to interpret it as a probability of material misstatement. Our findings indicate that the Belief-Function model accurately reflect the views of the auditor regarding the assurance obtained on the engagement in testing the accounts receivable area. Sensitively analyses revealed that the model can be used to assess the assurance provided by a given test or set of tests in attaining a cost-effective audits. A working prototype has been implemented and tested indicating that belief - function model would result in cheaper audits, timely reports among other benefits. However, the model has some applicability limitations that require some improvements and more research.
    URI
    http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/20208
    Publisher
    School of Computing and Informatics
    Collections
    • Faculty of Science & Technology (FST) [4213]

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