Aggregating Audit Evidence with a Markov Tree: An Application of Inventory Account Auditing in Turkey


  •  Alper Karavardar    

Abstract

Belief functions approach has a significant interest in audit literature. In this article, we present an evidential network for aggregating audit evidence based on belief function approach. This evidential network represents the structure of audit evidence for inventory account auditing. We used propagation of belief functions in an evidential network for aggregation of audit evidence. This approach help the auditors make an efficient and effective audit and it shows the relationship between audit evidence and audit risk. This study consists of three steps. In the first step, we reviewed the auditing literature which has relevant issues with this study. In the second step, we briefly gave information about Markov tree construction process and belief functions. In the third step, we created an evidential network which illustrates aggregating process for inventory account audit in a manufacturing company in Turkey. For this aim, we constructed a Markov tree. We used real audit files and we made use of auditors who are responsible in this case. We compared our evidential network results and auditors opinions which are represented in audit report. According to our study’s result, our evidential network reflects auditors’ professional judgement based on the audit evidence.



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