Much prior research addressing fraud prevention and detection methods has addressed “red flags.” For example, Albrecht and Romney (1986) found in a survey of practicing auditors that 31 flags related to internal control were considered better predictors of fraud. The survey contained a list of 87 red flags. Loebbecke and Willingham (1988) offered a model that considers the probability of material financial statement misstatement due to fraud as a function of three factors:
(1) the degree to which those in authority in an entity have reason to commit management fraud;
(2) the degree to which conditions allow management fraud to be committed; and
(3) the extent to which those in authority have an attitude or set of ethical values that would facilitate their commission of fraud.
Loebbecke and Willingham (1989) used the red flags approach to develop another conceptual model to evaluate fraud probability. A survey instrument was used to query 277 audit partners of a big 6 firm. These researchers concluded that an auditor’s assessment of the client’s internal controls is significant in evaluating the probability of fraud. Pincus (1989) found that auditors who did not employ red flag checklists outperformed those who did in an experimental setting. In another study, auditors were found to hold different opinions concerning the degree of fraud risk indicated by various red flag indicators. Auditors with different client experience were found to possess different perceptions of the importance of a given red flag indicator (Hackenbrack, 1993).
Other researchers have investigated the effectiveness of various audit procedures in detecting fraud. Hylas and Ashton (1982) performed an empirical study of 281 errors requiring financial statement adjustments on 152 audits. These researchers found that analytical review procedures and discussions with clients predicted a large percentage of errors. Wright and Ashton (1989) investigated the fraud detection effectiveness of client inquiry, expectations based on prior years, and analytical reviews from a sample of 186 engagements involving 368 proposed audit adjustments. These researchers discovered that about half of the errors were signaled by the three procedures noted.
Blocher (1992) determined that only four out of 24 fraud cases were signaled by analytical procedures. Calderon and Green (1994) found that analytical procedures were the initial signal in 15 percent of 455 fraud cases. Kaminski and Wetzel (2004) performed a longitudinal examination of various financial ratios on 30 matched pairs of firms. Using chaos theory methodology, metric tests were run to analyze the behavior of time-series data. These researchers found no differences in the dynamics between fraudulent and non-fraudulent firms providing evidence of the limited ability of financial ratios to detect fraud.
Apostolou et al. (2001) surveyed 140 internal and external auditors on the fraud risk factors contained in SAS 82. They document management characteristics as the most significant predictor of fraud followed by client operating/financial stability features, and industry conditions. Chen and Sennetti (2005) apply a limited, industry-specific strategic systems auditing lens and a logistic regression model to a matched sample of 52 computer firms accused of fraudulent financial reporting by the SEC. The model achieved an overall prediction rate of 91 percent for fraud and non-fraud firms.
Moyes and Baker (2003) conducted a survey of practicing auditors concerning the fraud detection effectiveness of 218 standard audit procedures. Results indicate that 56 out of 218 procedures were considered more effective in detecting fraud. In general, the most effective procedures were those yielding evidence about the existence and/or the strength of internal controls.
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