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A. Korvin, Jerry Strawser, P. Siegel (1995)
An Application of Control System To Cost Variance AnalysisManagerial Finance, 21
K. Fanning, K. Cogger, R. Srivastava (1995)
Detection of management fraud: a neural network approachProceedings the 11th Conference on Artificial Intelligence for Applications
Zoe-Vonna Palmrose (1991)
Trials Of Legal Disputes Involving Independent Auditors - Some Empirical-EvidenceJournal of Accounting Research, 29
Awni Zebda (1984)
THE INVESTIGATION OF COST VARIANCES: A FUZZY SET THEORY APPROACH*Decision Sciences, 15
J. Costello (1991)
The Auditor's Responsibilities for Fraud Detection and Disclosure: Do The Auditing Standards Provide A Safe Harbor?Maine Law Review, 43
L. Zadeh (1996)
Fuzzy sets
Statement on Auditing Standards SAS No. 53 requires that the audit be designed to provide a reasonable assurance of detecting management fraud. Traditionally auditors have utilized personal, business, and economic red flags in risk analysis and audit planning. Touche Ross 1974, Coopers and Lybrand 1977, Price Waterhouse 1985, and SAS Nos. 6, 16, 17, and 53 discuss various red flags associated with management fraud. However, the authoritative literature does not provide any guidance on how to measure and combine red flags. The extant literature primarily measures red flags as yes or no type binary variables. However, red flags are fuzzy in nature and fuzzy set approach can be used to measure and combine red flags. The purpose of this paper is to provide a framework for the application of the theory of fuzzy sets to the problem of assessing the risk of management fraud using red flags. This approach can be used to capture the beliefs of one or several auditors concerning red flags and combine these beliefs to estimate the risk of management fraud. This approach can be extended to build fuzzy reasoning systems that assess the risk of management fraud.
Managerial Finance – Emerald Publishing
Published: Jun 1, 1997
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