The Use of Data Mining Techniques In Detecting Fraudulent Financial Statements: An Application On Manufacturing Firms

Year 2009, Volume: 14 Issue: 2, 157 - 170, 01.06.2009

  Yrd.Doç.Dr. H. Ali Ata Yrd.Doç.Dr. İbrahim H. Seyrek

Abstract

Keywords

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HİLELİ FİNANSAL TABLOLARIN TESPİTİNDE VERİ MADENCİLİĞİ TEKNİKLERİNİN KULLANIMI: İMALAT FİRMALARI ÜZERİNE BİR UYGULAMA

Abstract

Hileli finansal tabloların tespiti denetçiler için oldukça önemlidir. Bu tür hileli finansal tabloların tespit edilmesi oldukça zor olduğundan, denetçiler nicel ve nitel birçok teknik kullanmaktadırlar. Bu çalışmada denetçiler tarafından yaygın olarak bilinmeyen bazı veri madenciliği teknikleri, finansal tablolardaki hileleri tespit etmeye yardımcı olmak üzere kullanılmıştır. Çalışma İMKB’de işlem gören ve imalat sektöründe faaliyet gösteren 100 firmanın bilgilerine dayalı olarak gerçekleştirilmiştir. Araştırma sonucunda kaldıraç oranı ve aktif karlılık oranının finansal tablo hilesini tespit etmede önemli finansal oranlar olduğu belirlenmiştir

Keywords

Hile, Finansal raporlama, Hileli finansal tablolar, Veri madenciliği

References

• 1. Abdul Majid, G.F.A. and Tsui, J.S.L., “An Analysis of Hong Kong Auditors’ Perceptions of The Importance of Selected Red Flag Factors in Risk Assessment”, Journal of Business Ethics, Vol. 32, 2001, pp. 263- 274.
• 2. Ansah, S.O., Moyes, G.D., Oyelere, P.B. and Hay, D., “An Empirical Analysis of The Likelihood of Detecting Fraud in New Zealand”, Managerial Auditing Journal, Vol. 17, No. 4, 2002, pp. 192-204.
• 3. Apostolou, B., Hassell J. and Webber, S., “Management fraud risk factors: ratings by forensic experts”, The CPA Journal, October, 2001, pp. 48-52.
• 4. Apostolou, B., Hassell J., Webber, S. and Sumners, G.E., “The Relative Importance of Management Fraud Risk Factors”, Behavioral Research in Accounting, Vol. 13, 2001, pp. 1-24.
• 5. Beasley, M.S., Carcello, J.V. and Hermanson, D.R., Fraudulent Financial Reporting: 1987–1997. An Analysis of U.S. Public Companies, COSO, New York, 1999.
• 6. Bell,T. and Carcello, J., “A Decision Aid for Assessing The Likelihood of Fraudulent Financial Reporting”, Auditing: A Journal of Practice & Theory, Vol.9, No.1, 2000, pp. 169–178.
• 7. Blocher, E., The Role of Analytical Procedures in Detecting Management Fraud, Institute of Management Accountants, Montvale, NJ., 2002.
• 8. Braiotta, L., Audit Committee Handbook, NJ, USA, John Wiley&Sons, 2004.
• 9. Calderon, T.G. and Green, B.P., “Signaling Fraud by Using Analytical Procedures”, Ohio CPA Journal, Vol. 53, No. 2, 1994, pp. 27-38.
• 10. Chen, W.S. and Du, Y.K. “Using Neural Networks and Data Mining Techniques for The Financial Distress Prediction Model”, Expert Systems with Applications, Vol. 36 , 2009, pp. 4075–4086
• 11. Eining, M.M., Jones, D.R. and Loebbecke, J.K., “Reliance on Decision Aids: An Examination of Auditors’ Assessment of Management Fraud”, Auditing: A Journal of Practice & Theory, Vol.16, No.2,1997, pp. 1-19.
• 12. Elliott, R.K. and Willingham, J.J., Management Fraud: Detection and Deterrence, Petro celli Books, New York, 1980.
• 13. Hand, D., Mannila, H., and Smyth, P., Principles of Data Mining. MIT Press, Cambridge, MA, 2001.
• 14. Fanning, K.and Cogger, K., “Neural Network Detection of Management Fraud Using Published Financial Data”, International Journal of Intelligent Systems in Accounting, Finance & Management, Vol.7, No.1, 1998, pp. 21–24.
• 15. Green, B.P. and Choi, J.H., “Assessing The Risk of Management Fraud Through Neural-Network Technology”, Auditing:A Journal of Practice and Theory, Vol.16, No.1, 1997, pp.14–28.
• 16. Heiman-Hoffman, B.V., Morgan, P.K. and Patton, M.J., “The Warning Signs of Fraudulent Financial Reporting”, Journal of Accountancy, October, 1996, pp. 75-86.
• 17. Kirkos, E., Spathis, C. and Manolopoulos, Y. , “Data Mining Techniques for The Detection of Fraudulent Financial Statements”, Expert Systems With Applications, Vol. 32, No.4, 2007, pp. 995–1003.
• 18. Knapp C.A. and Knapp, M.C., “The Effects of Experience and Explicit Fraud Risk Assessment in Detecting Fraud with Analytical Procedure”, Accounting, Organizations and Society, Vol. 26, 2001, pp. 25-37.
• 19. Larose, D.T., Discovering Knowledge in Data: An Introduction to Data Mining, John Wiley & Sons, Inc., Hoboken, New Jersey, 2005, pp. 11-17
• 20. Loebbecke, K.J., Eining, M.M. and Willingham, J., “Auditors’ Experience with Material Irregularities: Frequency, Nature, and Detectability”, Auditing: A Journal of Practice & Theory, Vol. 9, No. 1, 1989, pp. 1-28.
• 21. Moyes, G.D. and Hasan, I., “An Empirical Analysis of Fraud Detection Likelihood”, Managerial Auditing Journal, Vol. 11, No. 3, 1996, pp. 41- 46.
• 22. Persons, O.S., “Using Financial Statement Data to Identify Factors Associated with Fraudulent Financial Reporting”, Journal of Applied Business Researc, Vol. 11, No. 3, 1995, pp. 38-46.
• 23. Romney, B.M.. Albrecht W.S. and Cherrington, D.J., “Auditors and The Detection of Fraud”, The Journal of Accountancy, May, 1980, pp. 63-69.
• 24. Spathis, C., “Detecting False Financial Statements Using Published Data: Some Evidence From Greece”, Managerial Auditing Journal, Vol. 17, No. 4, 2002, pp. 179–191.
• 25. Schilit, H., Financial Shenanigans: How to Detect Accounting Gimmicks and Fraud in Financial Reports, New York, USA, McGraw-Hill, 2002.
• 26. Summers, S.L. and Sweeney, J.T., “Fraudulent Misstated Financial Statements and Insider Trading: An Empirical Analysis”, The Accounting Review, Vol. 73, No.1, 1998, pp. 131–146.
• 27. Wallace, M.P., “Neural Networks and Their Application in Finance”, Business Intelligence, Business Intelligence Journal. July, 2008, pp. 67- 76.
• 28. Sun, J. and Li, H.,“Data Mining Method for Listed Companies’Financial Distress Prediction” Knowledge-Based Systems, Vol. 21, No.1, 2006, pp. 1–5.