Neural network detection of management fraud using published financial data

Fanning, Kurt M.; Cogger, Kenneth O.

doi:10.1002/(sici)1099-1174(199803)7:1<21::aid-isaf138>3.0.co;2-k

Cited by 259 publications

(118 citation statements)

References 45 publications

(48 reference statements)

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“…Second, given its infrequency, most auditors lack the experience necessary to detect it. Finally, managers are deliberately trying to deceive the auditors (Fanning and Cogger, 1998). For such managers who understand the limitations of an audit, standardauditing procedures may be insufficient.…”

Section: Detecting Falsified Financial Statementsmentioning

confidence: 99%

“…During the preliminary stage of an audit, a financial statement classified as fraudulent signals the auditor to increase substantive testing during fieldwork. Fanning and Cogger (1998) use an artificial neural network (ANN) to develop a model for detecting management fraud. Using publicly available predictors of fraudulent financial statements, they find a model of eight variables with a high probability of detection.…”

Section: Detecting Falsified Financial Statementsmentioning

confidence: 99%

“…The fraudulent activity of recording sales before they are earned may show as additional accounts receivable. We tested this by considering the ratio of accounts receivable to sales (REC=SAL; Fanning and Cogger, 1998;Green, 1991;Daroca and Holder, 1985). Accounts receivable and inventory depend on the subjective judgement involved in estimating uncollected accounts and obsolete inventory.…”

Section: Variablesmentioning

confidence: 99%

See 2 more Smart Citations

Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques

Spathis

Doumpos

Zopounidis

2002

European Accounting Review

179

133

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Falsifying financial statements involves the manipulation of financial accounts by overstating assets, sales and profit, or understating liabilities, expenses or losses. This paper explores the effectiveness of an innovative classification methodology in detecting firms that issue falsified financial statements (FFS) and the identification of the factors associated to FFS. The methodology is based on the concepts of multicriteria decision aid (MCDA) and the application of the UTADIS classification method (UTilités Additives DIScriminantes). A sample of 76 Greek firms (38 with FFS and 38 non-FFS) described over ten financial ratios is used for detecting factors associated with FFS. A jackknife procedure approach is employed for model validation and comparison with multivariate statistical techniques, namely discriminant and logit analysis. The results indicate that the proposed MCDA methodology outperforms traditional statistical techniques which are widely used for FFS detection purposes. Furthermore, the results indicate that the investigation of financial information can be helpful towards the identification of FFS and highlight the importance of financial ratios such as the total debt to total assets ratio, the inventories to sales ratio, the net profit to sales ratio and the sales to total assets ratio.

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Section: Detecting Falsified Financial Statementsmentioning

confidence: 99%

Section: Detecting Falsified Financial Statementsmentioning

confidence: 99%

Section: Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques

Spathis

Doumpos

Zopounidis

2002

European Accounting Review

179

133

View full text Add to dashboard Cite

show abstract

“…From the research that examines similar classifi cation problems, like bankruptcy prediction or fraud detection, it is observed that methodologies derived from AI perform at least to the same level as statistical techniques (Fanning and Cogger, 1998;O'Leary, 1998;Lin and McLean, 2001). In the fi eld of auditing, relevant studies have only recently employed AI techniques, such as NNs (e.g.…”

Section: Prior Researchmentioning

confidence: 99%

“…Three methods derived from AI will be used here for the fi rst time, namely decision trees (DTs), neural networks (NNs) and k-nearest neighbours. These methods were selected on the basis of their previous successful application in auditing-related topics (Fanning and Cogger, 1998;Lin and McClean, 2001;Gaganis et al, 2007b;Kirkos et al, 2007a,b). The models developed are compared in terms of their accuracy rate.…”

mentioning

confidence: 99%

Audit‐firm group appointment: an artificial intelligence approach

Kirkos

Spathis

Manolopoulos

2009

Intelligent Systems in Accounting, Finance and Management

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SUMMARYAuditor appointment can be regarded as a matter of pursued audit quality and is driven by several factors. The adoption of an effective auditor procurement process increases the likelihood that a company will engage the right auditor at a fair price. In this study, three techniques derived from artifi cial intelligence (AI) are used to propose models capable of discriminating between cases where companies appoint a Big 4 or a Non-Big 4 auditor. These three AI methods are then compared with the broadly used method of logistic regression. The results indicate that two of the AI techniques outperform logistic regression. In addition, one method further improves its performance by applying bagging. Finally, signifi cant factors associated with auditor appointment are revealed.

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Risk assessment in internal auditing: a neural network approach

Ramamoorti

Bailey

Traver

1999

Int. J. Intell. Syst. Acc. Fin. Mgmt.

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Risk assessment is a systematic process for integrating professional judgments about relevant risk factors, their relative significance and probable adverse conditions and/or events leading to identification of auditable activities (IIA, 1995, SIAS No. 9). Internal auditors utilize risk measures to allocate critical audit resources to compliance, operational, or financial activities within the organization (Colbert, 1995). In information rich environments, risk assessment involves recognizing patterns in the data, such as complex data anomalies and discrepancies, that perhaps conceal one or more error or hazard conditions (e.g. Coakley and Brown, 1996; Bedard and Biggs, 1991; Libby, 1985). This research investigates whether neural networks can help enhance auditors' risk assessments. Neural networks, an emerging artificial intelligence technology, are a powerful non-linear optimization and pattern recognition tool (Haykin, 1994; Bishop, 1995). Several successful, real-world business neural network application decision aids have already been built (Burger and Traver, 1996). Neural network modeling may prove invaluable in directing internal auditor attention to those aspects of financial, operating, and compliance data most informative of high-risk audit areas, thus enhancing audit efficiency and effectiveness. This paper defines risk in an internal auditing context, describes contemporary approaches to performing risk assessments, provides an overview of the backpropagation neural network architecture, outlines the methodology adopted for conducting this research project including a Delphi study and comparison with statistical approaches, and presents preliminary results, which indicate that internal auditors could benefit from using neural network technology for assessing risk.

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Neural network detection of management fraud using published financial data

Cited by 259 publications

References 45 publications

Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques

Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques

Audit‐firm group appointment: an artificial intelligence approach

Risk assessment in internal auditing: a neural network approach

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