Article2009

Classification techniques for the identification of falsified financial statements: a comparative analysis

82 citations0/0 files storedIntelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management

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Auditing, Earnings Management, Governance Financial Distress and Bankruptcy Prediction Benford’s Law and Fraud Detection

Abstract

Abstract In this study, I develop 10 alternative classification models using logit analysis, discriminant analysis, support vector machines, artificial neural networks, probabilistic neural networks, nearest neighbours, UTADIS and MHDIS for the detection of falsified financial statements. The models are developed using financial and nonfinancial data. The sample includes 398 financial statements, half of which were assigned a qualified audit opinion. I compare these alternatives methods using out‐of‐time and out‐of‐sample tests. The results are used to derive conclusions on the performance of the methods and to investigate the potential of developing models that will assist auditors in identifying fraudulent financial statements. Copyright © 2009 John Wiley & Sons, Ltd.

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References

1981Auditor size and audit quality 1997An Empirical Analysis of the Relation between the Board of Director Composition and Financial Statement Fraud 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 1995Auditor brand name reputations and industry specializations 1986Predicting takeover targets 2000Does size matter? The influence of large clients on office-level auditor reporting decisions 1997Earnings disclosures and stockholder lawsuits 1993Auditing Standards, Legal Liability, and Auditor Wealth

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