Academic journal article Auditing: A Journal of Practice & Theory

A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting

Academic journal article Auditing: A Journal of Practice & Theory

A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting

Article excerpt

SUMMARY

The auditor's responsibility for detecting fraudulent financial reporting is of continuing importance to both the profession and society. The Auditing Standards Board has recently issued SAS No. 82, Consideration of Fraud in a Financial Statement Audit, which makes the auditor's responsibility for the detection of material fraud more explicit without increasing the level of responsibility.

Using a sample of 77 fraud engagements and 305 nonfraud engagements, we develop and test a logistic regression model that estimates the likelihood of fraudulent financial reporting for an audit client, conditioned on the presence or absence of several fraud-risk factors. The significant risk factors included in the final model are: weak internal control environment, rapid company growth, inadequate or inconsistent relative profitability, management places undue emphasis on meeting earnings projections, management lied to the auditors or was overly evasive, the ownership status (public vs. private) of the entity, and an interaction term between a weak control environment and an aggressive management attitude toward financial reporting. The logistic model was significantly more accurate than practicing auditors in assessing risk for the 77 fraud observations. There was not a significant difference between model assessments and those of practicing auditors for the sample of nonfraud cases.

These findings suggest that a relatively simple decision aid performs quite well in differentiating between fraud and nonfraud observations. Practitioners might consider using this model, or one developed using a similar procedure, in fulfilling the SAS No. 82 requirement to "assess the risk of material misstatement of the financial statements due to fraud."

Key Words: Fraudulent financial reporting, Decision aid.

Data Availability: Due to the confidential nature of this client information, the authors cannot release the data.

SAS No. 82, Consideration of Fraud in a Financial Statement Audit, provides guidance on the auditor's responsibility to "plan and perform the audit to obtain reasonable assurance about whether the financial statements are free of material misstatement, whether caused by error or fraud" (AICPA 1997). Fraud includes both fraudulent financial reporting and misappropriation of assets. The focus of this paper is on fraudulent financial reporting. (1)

Prior studies have found that failing to detect fraudulent financial reporting can expose the auditor to adverse legal and/or regulatory consequences. For example, Carcello and Palmrose (1994) found a significant positive association between the presence of a financial-reporting irregularity and litigation against the auditor. Also, Feroz et al. (1991) found that an auditor's failure to consider a client's fraud potential was cited in 20 percent of the Accounting and Auditing Enforcement Releases brought against auditors. Given the adverse legal and regulatory consequences to auditors from failing to detect fraudulent financial reporting, research that can help auditors assess fraud risk is of interest to both academics and practitioners.

This study has two objectives. First, we provide evidence on the efficacy of a decision aid that could be used to assess the risk of fraudulent financial reporting. The logistic regression model reported in the paper can provide an independent assessment of the likelihood of fraudulent financial reporting that can complement the auditor's unaided risk assessment. Our model embodies a set of fraud-risk factors that demonstrated better classificatory power than a large number of alternative sets evaluated during the study. In using the model, the auditor first assesses whether these "best-model" risk factors are present for the client. The decision aid then weights and combines these individual judgments into an overall assessment of the probability of fraudulent financial reporting. …

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