A rule‐based fuzzy reasoning system for assessing the risk of management fraud

Detecting management fraud and assessing the risk of management fraud are significant issues confronting the auditing profession. Considerable theoretical and empirical research (Loebbecke, Eining, and Willingham, 1989; Bell, Szykowny, and Willingham, 1993; Fanning, Cogger, and Srivastava, 1995; and Hansen, McDonald, Messier, and Bell, 1996) has been accomplished investigating these issues. Building on this research, we demonstrate the construction of a rule‐based fuzzy reasoning system to assess the risk of management fraud. The paper illustrates how fuzzy sets can be used intuitively to measure red flags on a categorical or interval scale, how different red flags can be combined using fuzzy rules, and how a single measure of the risk of management fraud can be derived. The knowledge base for this fuzzy reasoning system is developed by using the causal model of management fraud developed by Loebbecke, Eining and Willingham (1989), the empirical investigation of this model by Bell, Szykowny, and Willingham (1993), other researchers’ efforts and the authors’ judgments, using XpertRule software. The fuzzy reasoning system is tested using the fraud data provided by KPMG Peat Marwick. We discuss methods to magnify the knowledge base of this fuzzy reasoning system to make it a viable auditing tool, the costs and benefits of building a fuzzy reasoning system, and further extensions of this research. Copyright © 1998 John Wiley & Sons, Ltd.