Farklı Sektörlerden Türk Küçük ve Orta Büyüklükteki İşletmelerin(KOBİler) Hileli Finansal Tablolarının İncelenmesi

Öz

Finansal tablolarda hile, işletmelerin ve endüstrilerin sürdürülebilir finansal gelişimi üzerinde olumsuz etkilere sahiptir. Bu çalışmada, finansal tablolardaki hile tespiti açısından farklı sektörlerdeki Türk KOBİ'leri incelenmiştir. KOBİ'lerin kredi taleplerini değerlendiren bankaların finansal muhasebe dolandırıcılığı ile mücadele konusunda ihtiyatlı ve yenilikçi olmaları gerekmektedir. Bunlara dayanarak, farklı sektörlerden (imalat, inşaat, ulaşım, tarım) 341 Türk KOBİ'sinin finansal tabloları incelenerek kapsamlı bir karşılaştırma çalışması yapılmıştır. KOBİ'lerin verileri, Türkiye'nin aktif bazda en büyük bankalarından biri olan ve kendilerine finansman sağlamak için kredi sağladığı bir bankadan alınmıştır. Sonuçlar, inşaat sektöründeki firmaların (% 56 oranında) Kasa (100), Ortaklardan Alacaklar (131) finansal hesaplarını diğer sektörlerdeki firmalardan daha fazla manipüle ettiğini göstermektedir. Bu çalışma, hileli finansal raporlamada en çok hangi finansal hesapların kullanıldığını sektörel bazda anlamada, KOBİ'ler için dolandırıcılık risklerini azaltmak ve finansal raporlama sisteminin tüm oyuncuları için dolandırıcılıktan korunmak ve önlemek için faydalı olabileceği için yol gösterici olabilir.

Anahtar Kelimeler

• Hileli Finansol Tablo
• KOBİ
• Finansal Muhasebe Hilesi

Examining Fraudulent Financial Statements of Turkish Small and Medium Enterprises (SMEs) from Different Sectors

Öz

Financial statement fraud has negative effects on sustainable financial development of businesses and industries. In this sudy, Turkish SMEs from different sectors have been examined in terms of fraud detection in financial statements. Banks, which assess the credit demands of SMEs, must be vigilant and innovative to combat financial accounting fraud. From these standpoints, comprehensive comparison study is conducted by examining 341 Turkish SMEs' financial statements from different sectors (manufacturing, construction, transportation, agricultural). The data of these SMEs are collected from one of the largest banks, based on total assets, in Turkey in which the bank provide them loans for funding. Results show that firms in construction sector mostly manipulate Cash (100), Due from Shareholders (131) financial accounts more than firms in other sectors (with rate of 56 %). This study can be guideline to comprehend on sectoral basis which financial accounts are mostly used in fraudulent financial reporting in which it can be useful to reduce fraud risks for SMEs along with to protect and prevent against fraud for all players in financial reporting system.

Anahtar Kelimeler

• Fraudulent financial statement
• SME
• Financial Accounting Fraud

Kaynakça

1. Aladejebi, O., & Oladimeji, J. A. (2019). Fraud Management among Small and Medium Enterprises in Lagos, Nigeria. The Internatıonal Journal Of Busıness & Management, 7 (3), 227-236. DOI: 10.24940/theijbm/2019/v7/i3/BM1903-048
2. Andoh, C., Quaye D., & Frimpong, I.A. (2018). Impact of fraud on Ghanaian SMEs and coping mechanisms. Journal of Financial Crime, 25 (2), 400-418. DOI: https://doi.org/10.1108/JFC-05-2017-0050
3. Aris, N.A., Arif, S.M.M., Othman,R., & Zain, M.M. (2015). Fraudulent financial statement detection using statistical techniques: The case of small medium automotive enterprise. The Journal of Applied Business Research, 31 (4), 1469-1478. DOI: https://doi.org/10.19030/jabr.v31i4.9330
4. Birol, B. (2017). Corporate Governance and Fraud Detection: A Study from Borsa Istanbul. PhD Thesis, Yeditepe University, Turkey, 103-112.
5. Chen, S. (2016). Detection of fradulent financial statements using the hybrid data mining approach, Springer Plus, (2016) 5-89. https://springerplus.springeropen.com/articles/10.1186/s40064-016-1707-6
6. Chen, S., Goo, Y.J., & Shen, Z.D. (2014). A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements. The Scientific World Journal,2014. DOI: https://doi.org/10.1155/2014/968712
7. Cobo, E. P., Crespo, A.H., & Corte, J.M. (2017). Are credit risk analysts concerned about the audit of the financial statements of SMEs? Universia Business Review , 2017 (1), 1698-5117. DOI: 10.3232/UBR.2017.V14.N1.04
8. European Federation of Accountants (2005). How SMEs can reduce the Risk of Fraud. Retrieved from https://www.pibr.org.pl/assets/file/943,FEE-How-SMEs-can-reduce-the-Risk-of-Fraud.pdf

9. European Statistics (2008). NACE Rev. 2 Introductory Guidelines. Retrieved from https://circabc.europa.eu/sd/a/aaea2438-5405-43ae-866f-904923ab8ec2/NACE%20Rev.%202%20Introductory%20guidelines%20-%20EN.pdf
10. Fanning, K.M., & Cogger, K.O. (1998). Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management, 7(1998), 21-41. DOI: https://doi.org/10.1002/(SICI)1099-1174(199803)7:1<21::AID-ISAF138>3.0.CO;2-K
11. Fidan, M. E., & Mumcu, E. Ş. (2019). Internal Control and Fraud Risks in Mining Companies: A Study on a Marble Enterprise. KMU Journal of Social and Economic Research, 21(37), 61-81. https://dergipark.org.tr/tr/pub/kmusekad/issue/51332/615845
12. Girgenti, R.H.J.D., & Hedley, T.P. (2011). Managing the risk of fraud and misconduct: meeting the challenges of a global, regulated, and digital environment. McGrawHill, New York.
13. Gunduz, M., & Önder, O. (2013). Corruption and internal fraud in the Turkish construction industry. Science and Engineering Ethics, 19(2), 505–528. https://link.springer.com/article/10.1007/s11948-012-9356-9
14. Halıcı, A., & Erhan, D.U. (2013). Structuring strategic management with ratio analysis method: A case study in the transition to SME TFRS process. Procedia - Social and Behavioral Sciences, 99, 947 – 955.
15. Hess, M. F., & Cottrell Jr., J. H. (2016). Fraud risk management: A small business perspective. Business Horizons, 59(1), 13-18. DOI: 10.1016/j.bushor.2015.09.005
16. International Auditing and Assurance Standards Board (IAASB) (2009). International Standard on Auditing 240 (ISA 240): The Auditors’ Responsibilities Relating to Fraud in an Audit of Financial Statement, http://www.ifac.org/system/files/downloads/a012-2010-iaasb-handbook-isa-240.pdf.
17. Johnson, G.G., & Rudesill, C.L. (2001). An investigation into fraud prevention and detection of small businesses in the United States: Responsibilities of auditors, managers and business owners. Accounting Forum, 25(1), 56-78. DOI: 10.1111/1467-6303.00055
18. Jofre, M. (2017). Fighting Accounting Fraud through Forensic Analytics. Doctoral Dissertation, The University Of Sydney Business School, Australia, 42-119. http://hdl.handle.net/2123/17826
19. Kaminski, K.A., Wetzel, T.S., & Guan, L. (2004). Can financial ratios detect fraudulent financial reporting? Managerial Auditing Journal, 19(1), 15-28. DOI: https://doi.org/10.1108/02686900410509802
20. Karahan, M., İğde, M., & Özbezek, D. (2017). Evaluation of International Financial Reporting Standards in Terms of SMEs-Turkey Application. Gaziantep University Journal of Social Sciences, 16 (2), 330-344. DOI: https://doi.org/10.21547/jss.299362
21. Kassem, R. (2016). Detecting Financial Reporting Fraud: The Impact and Implications of Management Motivations for External Auditors – Evidence from the Egyptian Context. Doctoral Dissertation, Loughborough University, Loughborough Leicestershire, UK, 1-11.
22. Loebbecke, J.K., Eining, M.M., & Willingham, J.J. (1989). Auditors' experience with material irregularities-frequency, nature, and detectability. Auditing- A Journal of Practice & Theory, 9(1), 1-28.
23. Mekic, A., Halilbegovic, S., & Huric, A. (2017). Forensic Accounting as a Solution to Manipulative Accounting of Sme’s in Bosnia and Herzegovina. Ecoforum, 2 (11). http://www.ecoforumjournal.ro/index.php/eco/article/view/634
24. Ngai, E.W.T., Hu,Y., Wong, Y.H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50 (2011), 559–569. DOI: https://doi.org/10.1016/j.dss.2010.08.006
25. Omar, N., Johari, Z.A., & Smith, M. (2017). Predicting fraudulent financial reporting using artificial neural network. Journal of Financial Crime, 24(2), 362-387. DOI:https://doi.org/10.1108/JFC-11-2015-0061
26. Persons, O.S. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research, 11(3), 38–46. DOI: https://doi.org/10.19030/jabr.v11i3.5858
27. Public Oversight Accounting and Auditing Standards Authority (2018). Regulations, http://www.kgk.gov.tr/DynamicContentDetail/7954/Regulations.
28. Ravisankar, P., Ravi, V., Rao, G.R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50 (2011), 491–500. DOI: https://doi.org/10.1016/j.dss.2010.11.006
29. Rezaee, Z. (2002). The three Cs of fraudulent financial reporting. Internal Auditor, 57-61.
30. Rezaee, Z., & Riley, R. (2010). Financial statement fraud prevention and detection. Second Edition, John Wiley&Sons,Inc., Hoboken, New Jersey and Canada.
31. Salijeni, G. , Taddei, A.S., & Turley, S. (2019). Big Data and changes in audit technology: contemplating a research agenda. Accounting and Business Research, 49(1), 95-119. DOI: https://doi.org/10.1080/00014788.2018.1459458
32. Shanmugam, J.K., Ali, A., & Haat, M.H.C. (2012). Internal control, risk management and fraud prevention measures on SMEs: reliability and validity of research instrument. 3rd International Conference on Business and Economic Research, 12 - 13 March , Indonesia.
33. Sow, A.N., Basiruddin, R., Mohammad, J., Abdul & Rasid, S.Z. (2018a). Fraud prevention in Malaysian small and medium enterprises (SMEs). Journal of Financial Crime, 25(2), 499-517. DOI: https://doi.org/10.1108/JFC-05-2017-0049
34. Sow, A.N., Basiruddin, R., Abdul Rasid, S.Z., & Husin, M. M. (2018b). Understanding fraud in Malaysian SMEs. Journal of Financial Crime, 25(3), 870-881. DOI: https://doi.org/10.1108/JFC-08-2017-0077
35. Spathis, C.T. (2002). Detecting false financial statements using published data: some evidence from Greece. Managerial Auditing Journal, 17(4), 179-191. DOI: https://doi.org/10.1108/02686900210424321
36. Summers, S.L., & Sweeney, J.T. (1998). Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 73(1), 131-146.
37. Tazilah, M.D.A.B.K., & Hussain, N.B.C. (2015). The importance of internal control in SMEs: Fraud prevention & detection. International Conference on Business, Accounting, Finance, and Economics, Malaysia, 9th October.
38. Thiruvadi, S., & Patel, S. C. (2011). Survey of data mining techniques used in fraud detection and prevention. Information Technology Journal, 10 (4), 710-716. DOI: 10.3923/itj.2011.710.716
39. Turkmen, B. (2016). Errors and abuses in financial accounting and results. Procedia Economics and Finance, 38(2016), 77-83. DOI: 10.1016/S2212-5671(16)30179-4
40. West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: a comprehensive review. Computers & Security, 57, 47–66. DOI: https://doi.org/10.1016/j.cose.2015.09.005