The Belief-Function Approach to Aggregating Audit Evidence
Issue Date
1995Author
Srivastava, Rajendra P.
Publisher
Wiley
Type
Article
Article Version
Scholarly/refereed, author accepted manuscript
Metadata
Show full item recordAbstract
In this article, we present the belief-function approach to aggregating audit evidence. The
approach uses an evidential network to represent the structure of audit evidence. In turn, it
allows us to treat all types of dependencies and relationships among accounts and items of
evidence, and thus the approach should help the auditor conduct an efficient and effective
audit. Aggregation of evidence is equivalent to propagation of beliefs in an evidential network.
The paper describes in detail the three major steps involved in the propagation process. The
first step deals with drawing the evidential network representing the connections among
variables and items of evidence, based on the experience and judgment of the auditor. We then
use the evidential network to determine the clusters of variables over which we have belief
functions. The second step deals with constructing a Markov tree from the clusters of variables
determined in step one. The third step deals with the propagation of belief functions in the
Markov tree. We use a moderately complex example to illustrate the details of the aggregation
process.
Description
This is the author's final draft. The publisher's official version is available from: <http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291098-111X>
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Citation
Srivastava, Rajendra. (1995) The Belief-Function Approach to Aggregating Audit Evidence.
International Journal of Intelligent Systems, 10 (3), 329-356.
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