Formulation of Financial Statement Fraud Detection Model Using Artificial Neural Network and Support Vector Machine Approaches in Companies Listed in Tehran Bahador Stock Exchange

Document Type : Original Article

Authors
... .. 1
.. .. 2
1 .
2 Department of Accounting, Faculty of Accounting and Management, Hormozgan University, Bandar-e-Abbas, Iran
Abstract
Formulation of Financial Statement Fraud Detection Model Using Artificial Neural Network and Support Vector Machine Approaches in Companies Listed in Tehran Bahador Stock Exchange
Abstract:
Nowadays, due to the increasing expansion of financial markets and the need to attract domestic and foreign investors, one of the most important concerns of any company is to finance the correct disclosure of financial information. Artificial and support vector machines were listed at companies listed on the Tehran Stock Exchange. The present study is a kind of applied and empirical-correlation research. The statistical population includes the companies listed in the Tehran Stock Exchange during the period 1972-1999. Recent research has shown that investors in the decision-making process select companies whose profitability is higher and in fact higher quality. The results showed that in the training section the predictive power of the support vector algorithm was about 86% and in the test about 82%. Also, the prediction power of neural network algorithm was 81% in training and 78% in test.
Keywords
fraud
financial statements
artificial neural network
support vector machine
  • پور ﺣﻴﺪری ، ﺑﺬر اﻓﺸﺎن ، 1391 .ﺑﺮرﺳﻲ اﺛﺮ وﻳﮋﮔﻲ ﻫﺎی ﻓﺮدی ﺣﺴﺎﺑﺮﺳﺎن درﺗﻌﻴﻴﻦ اﻫﻤﻴﺖ ﺑﺴﺘﺮﻫﺎی ﺧﻄﺮ ﺗﻘﻠﺐ. ﻓﺼﻠﻨﺎﻣﻪ . ﭘﮋوﻫﺶ ﻫﺎی ﺗﺠﺮﺑﻲ ﺣﺴﺎﺑﺪاری ﻣﺎﻟﻲ، ص 52-28.
  • رستمی، محمد رضا.، جعفری درگیری، اعظم، 1392. بررسی قانون بن فورد در بورس اوراق بهادر، پژوهشهای مدیریت در ایران. 17 (1): 110-95.
  • شیخ، محمد جواد 1383، ﺑﺮرﺳﯽ اﻫﻤﯿﺖ ﺷﺎﺧصﻫﺎی ﺗﻘﻠﺐ ﺑﺎﻟﻘﻮه ﻣـﺎﻟﯽ ﺑـﺎ اﺳـﺘﻔﺎده ازﺗﮑﻨﯿﮏ ﺗﺤﻠﯿﻞ ﻋﺎﻣﻠﯽ در ﺷﺮﮐﺘﻬﺎی اﯾﺮاﻧﯽ، رﺳﺎﻟﻪ دﮐﺘﺮی ﺣﺴﺎﺑﺪاری،داﻧﺸﮕﺎه ﺗﻬﺮان،داﻧﺸـﮑﺪه ﻣﺪﯾﺮﯾﺖ.
  • فرقاندوست حقیقی، برواری، فرید.(1388). بررسی کاربرد روشهای تحلیلی در ارزیابی ریسک تحریف صورتهای مالی تقلب مدیریت،فصلنامه دانش وپژوهش حسابداری، شماره شانزدهم.
  • Association of Certified Fraud Examiners (ACFE). (2014). Report to the nations on occupational fraud and abuse. Austin, TX: The association of certified fraud examiners, Inc.
  • Association of Certified Fraud Examiners (ACFE). (2016). Report to the nations on occupational fraud and abuse. Austin, TX: The association of certified fraud examiners, Inc.
  • Association of Certified Fraud Examiners. (2014). Fraud examiners manual. Austin, ACFE
  • Ata A., Ibrahim H. Seyrek(2009). "the Use of Data Mining Techniques in Detecting Fraudulent Financial Statements: An Application on Manufacturing Firms, Suleyman Demirel University". The Journal of Faculty of Economics and Administrative Sciences, Vol. 14(2), pp. 157-170.
  • Ata A., Ibrahim H. Seyrek, the Use of Data Mining Techniques in Detecting Fraudulent Financial Statements: An Application on Manufacturing Firms, Suleyman Demirel University, The Journal of Faculty of Economics and Administrative Sciences, Vol. 14(2), 2009, pp. 157-170
  • Autore, D. M., Hutton, I., Peterson, d. R., & Smith, I. H. (2014). The effect of securities litigation on external financing, Journal of Corporate Finance, 27, 231-250. Doi:10.1016/j.jcorpfin.2014.05.007.
  • Azrina, M. Y., & Ling Lai, M. (2014). An integrative model in predicting corporate tax Journal of Financial Crime, 21(4), 424. 
  • based on data-mining techniques. IJCA International Journal of Computer Applications, 39(1), 37-47.
  • Beasley, M.S., Carcello, J.V., Hermanson, D.R., and Neal, T.L. (2010). Fraudulent financial reporting 1998-2007: An Analysis of U.S. Public Companies. Retrieved from http://www.coso.org/documents/COSOFRAUDSTUDY2010_001.pdf
  • Bei, Z., & Wijewardana, W. P. (2012). Working capital policy practice: Evidence from Sri Lankan companies. Procedia-Social and Behavioral Sciences, 40, 695-700.
  • Beneish, M. D. (1997). Detecting GAAP violation: Implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy Journal of Accounting and Public Policy, 16(3), 271309.
  • Bhattacharjee, S., Maletta, M. J., & Moreno, K. K. (2016). The Role of Account Subjectivity and Risk of Material Misstatement on Auditors' Internal Audit Reliance Judgments. Accounting Horizons, 30(2), 225-238. doi:10.2308/acch51363 Bishop, C. C., Hermanson, D. R., & Houston, R. W. (2013). PCAOB Inspections of International Audit Firms: Initial Evidence. International Journal Of Auditing, 17(1), 1-18. doi:10.1111/j.1099-1123.2012.00453.x
  • Blay, A. D., Sneathen Jr, L. D., & Kizirian, T. (2007). The effects of fraud and going‐concern risk on auditors' assessments of the risk of material misstatement and resulting audit procedures. International Journal of Auditing, 11(3), 149-163.
  • Bonner, S.E., Palmrose, Z.V. and Young, S.M. (١٩٩٨) , ``Fraud type and auditor litigation. An analysis of SEC accounting and auditing enforcement releases’’, The Accounting Review, Vol. ٧٣ ٤, pp. ٥٠٣-٣٢.
  • Boritz, J. E., Kochetova-Kozloski, N., & Robinson, L. (2015). Are fraud specialists relatively more effective than auditors at modifying audit programs in the   presence of fraud risk? Accounting Review, 90(3), 881-915. doi:10.2308/accr-  50911
  • Boučková, M. (2015). Management Accounting and Agency Theory, Procedia Economics and Finance, 25, 5-13. doi: 10.1016/S2212-5671(15)00707-8.
  • Boyle, D. N., Carpenter, B. W., & Hermanson, D. R. (2012). CEOs, CFOs, and Accounting Fraud. CPA Journal, 82(1), 62-65. 
  • Bui, T., & Amaria, P. (2014). How do financial analysts in Vietnam perceive the relationship between the corporate board structure and financial statement fraud? International Journal of Business, Accounting, and Finance, 8 (1), 1-23.
  • Burton, F. G., Wilks, T. J., & Zimbelman, M. F. (2013). How auditor legal liability influences the detection and frequency of fraudulent financial reporting. Current Issues in Auditing, 7 (2), 9-15. doi:10.2308/ciia-50566
  • Carpenter, T. D., & Reimers, J. L. (2005). Unethical and fraudulent financial reporting: Applying the theory of planned behavior. Journal of Business Ethics, 60(2), 115129. doi: 10.1007/s10551-004-7370-9
  • Cecchini, M., Aytug, H., Koehler, G. J., and Pathak, P. (2010). Making words work: using financial text as a predictor of financial events. Decision Support Systems, 50(1), 164-175.
  • Chen W.S. and Y.K. Du(2010). "Using Neural Networks and Data Mining Techniques for The Financial Distress Prediction Model". Expert Systems with Applications, Vol. 36, pp.4075–4086. Elliott R.K., and J.J. Willingham(1980). Management Fraud: Detection and Deterrence, Petro celli Books, NewYork, p.4.
  • Chen W.S. and Y.K. Du, Using Neural Networks and Data Mining Techniques for The Financial Distress Prediction Model, Expert Systems with Applications, Vol. 36, pp.4875– 4886, 2889.
  • Chen, S. (2016). Detection of fraudulent financial statements using the hybrid data mining approach. SpringerPlus, 5(1), 89.
  • Chen, S. (2016). Detection of fraudulent financial statements using the hybrid data mining approach. Springerplus, 589. doi:10.1186/s40064-016-1707-6
  • Clikeman, P. M., & Diaz, J. (2014). ABC Electronics: An Instructional case illustrating auditors' use of preliminary analytical procedures. Current Issues In Auditing, 8(1), I1-I10. doi:10.2308/ciia-50775
  • Cohen, J., Ding, Y., Lesage, C., & Stolowy, H. (2010). Corporate fraud and managers’ behavior: Evidence from the press. J Bus Ethics Journal of Business Ethics, 95(2), 271-315. doi: 10.1007/s10551-011-0857-2
  • Peytcheva, M. (2014). Professional skepticism and auditor cognitive performance in a hypothesis-testing task. Managerial Auditing Journal, 29(1), 27-49. doi: http://dx.doi.org/10.1108/MAJ-04-2013-0852
  • Peytcheva, M., & Warren, D. E. (2013). How auditors perceive sanction severity and the detection of violations: Insights into professional vulnerabilities. Accounting & The Public Interest, 13(1), 1-13. doi:10.2308/apin-10343
  • Plumlee, R. D., Rixom, B. A., & Rosman, A. J. (2015). Training auditors to perform analytical procedures using metacognitive skills. Accounting Review, 90(1), 351369. doi:10.2308/accr-50856
  • Polit-O'Hara D, Beck CT. Essentials of nursing research: Methods, appraisal, and utilization. Philadelphia: Lippincott Williams & Wilkins; 2006.
  • Power, M. (2013). The apparatus of fraud risk. Accounting, Organizations & Society, 38(6/7), 525-543. doi:10.1016/j.aos.2012.07.004
  • Public Company Accounting Oversight Board (PCAOB). (2016). PCAOB inspections. Retrieved from: https://pcaobus.org/inspections/Pages/default.aspx
  • Purda, L., & Skillicorn, D. (2015). Accounting variables, deception, and a bag of words: assessing the tools of fraud detection. Contemporary Accounting `Research,  32(3), 1193-1223. doi:10.1111/1911-3846.12089
  • Rauch, B., Göttsche, M., Brähler, G., & Kronfeld, T. (2014). Deficit versus social statistics: Empirical evidence for the effectiveness of the Benford’s law. Applied Economics Letters, 21(3), 147-151. doi:10.1080/13504851.2013.844319
  • 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(2), 491-500. doi:10.1016/j.dss.2010.11.006 
  • Reffett, A. B. (2010). Can identifying and investigating fraud risks increase auditors' liability? The Accounting Review, 85(6), 2145-2167. 
  • Robu, I., & Robu, M. (2013). Proceduri de audit pentru estimarea riscului de frauda bazate pe indici de detectare a manipularilor contabile/Audit procedures for estimating the fraud risk based on indexes for detection of accounting manipulation. Audit Financiar, 11(10), 3-16.
  • Roxas, M. L. (2011). Financial statement fraud detection using ratio and digital analysis. Journal of Leadership, Accountability & Ethics, 8(4), 56-66.
  • Ruankaew, T. (2016). Beyond the fraud diamond. International Journal Of Busines Management & Economic Research, 7(1), 474-476.
  • Hort, Beatriz De S. L. Pires De Lima And Carlos Cristiano H. Borges (2009).Data Preprocessing Of Bankruptcy Prediction Models Using Data Mining Techniques. Available on line At: Www.Blog.Campe.Com.Br/Wp-Content/Uploads/2009/03/Witpress_Conf-2.Pdf.
  • Sabau, A. S. (2012). Survey of clustering based financial fraud detection research. Informatica Economica, 16(1), 110-122.
  • Salisbury, L. T. (2013). Lack of professional skepticism reason audits fail to pass muster, says tempro. Accounting Policy & Practice Report, 9(20), 817-818.
  • Salkind, N. J. (2012). Exploring research. Boston: Prentice Hall
  • Santos, J. B., & Ledur Brito, L. A. (2012). Toward a subjective measurement model for firm performance. BAR - Brazilian Administration Review, 9(2), 95-117.
  • Santoso, N. T. (2018). Predicting Financial Statement Fraud with Fraud Diamond Model of Manufacturing Companies Listed in Indonesia. In State-of-the-Art Theories and Empirical Evidence (pp. 151-163). Springer, Singapore.
  • Schuchter, A., & Levi, M. (2015). Beyond the fraud triangle: Swiss and Austrian elite Accounting Forum, 39(3), 176-187. doi:10.1016/j.accfor.2014.12.001
  • Shamki, Dhia’a, (2009), "Internal Audit Responsibilities in Auditing Financial Systems Fraud", Business e-Bulletin, Vol. 1, Issue 1, PP. 25-32
  • Sharma, A. K., & Panigrahi, P. (2012). A review of financial accounting fraud detection
  • Simha, A., & Satyanarayan, S. (2016). Straight from the horse's mouth: Auditors' on fraud detection and prevention, roles of technology, and white-collars getting splattered with red! Journal Of Accounting & Finance (2158-3625), 16(1), 26-44.
  • Simon N,( 1881), Note on the Frequency of Use of the Different Digit in Natural numbers, American Journal of Mathematics, No. 4, pp. 39-40
  • Simon, C. A. (2012). Individual auditors' identification of relevant fraud schemes. Auditing: A Journal Of Practice & Theory, 31(1), 1-16. doi:10.2308/ajpt-10169
  • S (2002). Data Mining As A Financial Auditing Tool. Available on line At: Pafis.Shh.Fi/Graduates/Supsir01.Pdf.
  • Smith, G. S. (2015). The Past, present, and future of forensic accounting. CPA Journal, 85(3), 16-21.
  • Solomon, S. J. (2015). Let the numbers talk: A look into the use of the Benford’s analysis as a method of detecting data fraud. Academy Of Management Annual Meeting Proceedings, 1. doi:10.5465/AMBPP.2015.15089
  • Song, X., Hu, Z., Du, J., & Sheng, Z. (2014). Application of machine learning methods to risk assessment of financial statement fraud: Evidence from china. Journal of Forecasting, 33(8), 611-626. doi:10.1002/for.2294 
  • Stančić, P., Dimitrijević, D., & Stančić, V. (2013). Forensic accounting - the response of the profession to financial statement frauds. TEME: Casopis Za Društvene Nauke, 37(4), 1879-1897.
  • Summers, S.L. and Sweeney, J.T. (١٩٩٨) , ``Fraudulently misstated financial statements and insider trading: an empirical analysis’’, The Accounting Review, Vol. ٧٣ ١, pp. ١٣١-٤٦.
  • Sun, J., & Liu, G. (2016). Does analyst coverage constrain real earnings management? The Quarterly Review of Economics and Finance, 59, 131-140. doi:10.1016/j.qref.2015.03.009
  • Syriopoulos, T., & Tsatsaronis, M. (2012). Corporate governance mechanisms and financial performance: CEO duality in shipping firms. Eurasian Business Review, 2(1), 1-30.
  • Tabuena, J. (2014). Just what is "reasonable assurance"? Compliance Week, 11(121), 38-39.
  • Tamari, M. (1966). Financial ratios as a means of forecasting bankruptcy. Management International Review, 4.
  • Whitney, M. P. (2015). Review of Neurobiology and the development of human morality: Evolution, culture, and wisdom. Journal Of Moral Education, 44(4), 537-539. doi:10.1080/03057240.2015.1069479