Applying Rough Developed theoretical Models (ERST), Interpretation-Structural Analysis (ISM) and Decision Tree (CART) for Help Auditors to Identify Fraud in the Financial Statements of Companies Listed on the Stock Exchange of Iran

Subject Areas : Journal of Investment Knowledge

Davood Hasanpoor ¹ , hasan valiyan ² , mehdi safari griyly ³ , Reza Tahmasbizadeh ⁴

1 - Lecturer, Department of Accounting, Payam Noor University, Tehran, Iran
2 - Department of Accounting
3 - Department of Accounting
4 - Department of Accounting, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran

Received: 2018-05-04 Accepted : 2018-06-17 Published : 2020-04-20

Keywords: Set Theory Developed Using Model (ERST), Decision Tree Model (CART), Fraud in the Financial Statements,

Abstract :

The Purpose of this Research is Rough Set Theory Developed Using Model (ERST) to Assist Auditors to Identify Fraud in the Financial Statements of Iranian Companies Listed on Stock Exchange. The method of this combined research is based on the adaptation of theoretical foundations through the critical evaluation method to identify the characteristics and criteria of fraud in the financial statements (x) and the characteristics of committing fraud through them (y) and based on the decision tree (CART) and the developed Rough Theory Model (ERST) are seeking to determine the most effective criteria for fraud and how it can be applied in financial statements. The statistical population of the study consisted of 12 expert auditors selected through targeted and homogeneous sampling. In this study 18 indicators were identified as criteria for fraud and 5 attributes as ways of committing fraud. The results of this study showed that, based on the result of the management decision tree (CART) as the most important indicator of fraud, according to the developed Rough Theory Model (ERST), accounts receivable are considered as the most important feature of fraudulent behavior. Accordingly, in the conclusion of this research, for determining the fraud in the financial statements, we can use two indicators of low inventory sale (X12) and high management ownership (X17) based on changes in accounts receivable.

References:

آذر، ع.، خسروانی، ف.، جلالی، ر. (1392). تحقیق در عملیات نرم رویکردهای ساختاردهی، تهران: سازمان مدیریت صنعتی.

حساس یگانه، یحیی.، ظهیر، مصطفی.، غفاری، زهرا. (1395). بررسی رابطه میان نقش نظارتی حسابرسان و ارتقای سلامت نظام اداری، فصلنامه دانش حسابداری و حسابرسی مدیریت، سال پنجم، شماره 18، تابستان، 83-92.

خواجوی، شکرالله.، ابراهیمی، مهرداد. (1396). ارائة یک رویکرد محاسباتی نوین برای پیش بینی تقلب در صورت‌های مالی، با استفاده از شیوه‌های خوشه بندی و طبقه بندی (شواهدی از شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران)، مجله ی پیشرفت‌های حسابداری دانشگاه شیراز، دوره نهم، شماره دوم، پاییز و زمستان، پیاپی 3/73، 1-34.

خواجوی، شکرالله.، ابراهیمی، مهرداد. (1396). مدل سازی متغیرهای اثرگذار برای کشف تقلب در صورت‌های مالی با استفاده از تکنیک‌های داده کاوی، فصلنامه حسابداری مالی، سال نهم، شماره 33، بهار، 23-50.

رضایی پندری، عباس و یکه زارع، محسن. (1395). طراحی مدل ساختاری-تفسیری عوامل انتقال فناوری موفیت آمیز در راستای رسیدن به توسعه پایدار، پژوهش‌های مدیریت در ایران، دوره 20، شماره 1، بهار، 61-79.

محمدی مقدم؛ احسان.، معین الدین، محمود.، حیرانی، فروغ. (1397). شناسایی و رتبه بندی عوامل مؤثر بر احتمال بروز تقلب یا اعمال مجرمانه توسط حسابداران با استفاده از نظریه مثلث تقلب، فصلنامه علمی‌پژوهشی دانش حسابداری و حسابرسی مدیریت، سال هفتم، شماره 25، بهار، 123-138.

هاشمی، سید عباس.، حریری، امیر سینا. (1396). ارزیابی توانایی قانون بنفورد در شناسایی و پیش بینی کشف تقلب مالی، فصلنامه بررسی‌های حسابداری و حسابرسی، دوره 24، شماره 2، تابستان، 283-302.

American Institute of Certified Public Accountants (AICPA). (1997). "Consideration of fraud in a financial statement audit", Statement on Auditing Standards No. 82 New York.

Amiram, D., Bozanic, Z. & E. Roen. (2015). Financial Statement Errors: Evidence from the Distributional Properties of Financial Statement Numbers. Review of Accounting Studies, 20(4), 1540-1593.

Awang, Yunitam Suhaiza Ismail,Abdul Rahim Abdul Rahman. (2016). Measuring the potential for financial reporting fraud in a highly regulated industry the industry, The International Journal of Accounting and Business Society. Vol. 24, No. 1. Pp. 81- 97.

Beasley, M. (1996). "An empirical analysis of the relation between board of director composition and financial statement fraud", The Accounting Review, vol. 71, no. 4; 443-466.

Breiman, L., Friedman, J, H.,  Olshen, R, A., Stone, C. (1984). "Classification and regression trees", Wadsworth International Group: Belmont, California.

Caetano, S., Aires-de-Sousa, J., Daszykowski, M., Vander Heyden. Y. (2005). "Prediction of enantioselectivity using chiralitv codes and classification and regression Trees", Anaiytica Chimica Acta, vol. 544, no. I -2, pp 315-326.

Chen, G. (2006). "Positive research on the financial statement fraud factors of Listed companies in China", Journal of Modern Accounting and Auditing, vol. 2, no. 6; 25-34.

Chen, J., Cumming, D., Hou, W. and Lee, E. (2016).“Does the External Monitoring Effect of Financial Analysts Deter Corporate Fraud in China?”, Journal of Business Ethics, Vol.134 No. 4, pp.727-742

Eining M, Jones D and Loebbecke J. (1997). "Reliance on decision aids: An examination of auditors' assessment of management fraud". Auditing: A Journal of Practice and Theory; 16:1-19.

Fanning, K. M. and K. O. Cogger (1994). “A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction”, Intelligent Systems in Accounting, Finance and Management, Vol. 3, pp. 241-252.

Feroz, E. H., Kwon, T. M., Pastena, V. S., & Park, K. (2002). The efficacy of red flags in predicting the SEC’s targets: An artificial neural networks approach. International Journal of Intelligent Systems in Accounting, Finance and Management, 9, 145–157

Feroz, F., Park, K.,  Pastena, V. (1991). "The financial and market effects of the SECs accounting and auditing enforcement releases", Journal of Accounting Research, vol. 29, no. 3, ppI07-142.

Firth, M., Rui, O. M. and Wu, W. (2011). “Cooking the books: Recipes and costs of falsified financial statements in China”, Journal of Corporate Finance, Vol. 17 No. 2, pp. 371-390

Greco, S., Matarazzo, B., and Slowinski, R. 2001, “Rough sets theory for multicriteria decision analysis,” European Journal of Operational Research, Vol. 129, No. 1. Pp1-47.

Green, B. P. and J. H. Choi (1997). “Assessing the Risk of Management Fraud through Neural Network Technology.” Auditing: A Journal of Practice & Theory, Vol. 16, pp. 1428-.

Huang, S. H., Tsaih, R. H., & Yu, F. (2014). Topological pattern discovery and feature xxtraction for fraudulent financial reporting. Expert Systems with Applications, 41, 4360–4372.

Imai, sh., Weilin, Ch., Watada, J., Tzeng, G, H. (2008). Rough Sets Approach to Human Resource Development of Information Technology Corporations, IJSSST, Vol. 9, No. 2, May, 31-42.

Jitesh Thakkar, S.G. Veshmukh, A.V. Gupta anV Ravi Shankar, (2017). “Vevelopment of a balanceV scorecarV An integrateV approach of Interpretive Structural MoVeling (ISM) anV Analytic Network Process (ANP)”, International Journal of ProVuctivity anV Performance Management , 56 ( 1), 25-59.

Kaminski, K. A., Wetzel, T. S., & Guan, L. (2004). Can financial ratios detect fraudulent financial reporting? Managerial Auditing Journal, 19,15–28.

Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32, 995–1003

Knapp, C, A., Knapp, M, C. (2001). "The effects of experience and explicit fraud risk assessment in detecting fraud with analytical procedures", Accounting, Organizations and SOCiety, vol. 26, no. 1; 25-37.

Kotsiantis, S., Koumanakos, E., Tzelepis, D., Tampakas, V. (2016). "Forecasting fraudulent financial statements using data mining", International Journal of Computational Intelligence, vol. 3, no. 2; l04-110.

Lin, T, T. (2009). "A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models", Neurocomputing, vol. 72, no. 16, pp3507-3516.

Pai, P, F., Hsu, M, F., Wange, M, Ch. (2010). "Analyzing academic achievement of junior high school students by an improved rough set model", Computers and Education, vol. 54, no. 4, pp889-900.

Pai, P, F., Hsu, M, F., Wange, M, Ch. (2018). Computer-Assisted Audit Techniques based on an Enhanced Rough Set Model, Identify applicable sponsor ls here. (IEEE).

Pawlak, Z. 1982, “Rough sets,” International Journal of Computer and Information Science, Vol. 11, No. 5. Pp341–356.

Perols, J . L., Lougee, B, A. (2011).The relation between earnings management and financial statement fraud. Advances in Accounting, incorporating Advances in International Accounting, 27, 39-53.

Persons, O, S., Milind, S., Zeng, T., Uma, V. (2015). "The relation between the new corporate governance rules and likelihood of financial statement fraud", Review of Accounting and Finance, vol. 4, no. 2; I25-148.

Persons, O. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research, 11, 38–46.

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, 491-500

Segal, S.Y., (2016). Accounting frauds – review of advanced technologies to detect and prevent frauds. Economics and Business Review, 16 (4): 45–64.

Singh M.V., Shankar, R, Narain R , Agarwal, (2003), “An interpretive structural moVeling of knowleVge management in engineering inVustries”, Journal of AVvances in Management Research, 1, 28 – 40.

Spathis, C. (2006) "Detecting false financial statements using published data: some evidence from Greece", Managerial Auditing Journal, vo1. 17, noA, pp 179-191.

Spathis, C., Doumpos, M., & Zopounidis, C. (2002). Detecting falsified financial statements: A comparative study using multicriteria analysis and multivariate statistical techniques. European Accounting Review, 11(3), 509-535

Spathis, C., Doumpos, M., Zopounidis, C.  (2002). "Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques", The European Accounting Review, vol. II, no. 3, pp509-535.

Spathis, C., Doumpos, M., Zopounidis, C. (2002). "Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques", The European Accounting Review, vol. II, no. 3, pp509-535.

Wei, Y., chen, J., Wirth, C. (2017). "Detecting fraud in Chinese listed company balance sheets", Pacific Accounting Review, https://doi.org/10.1108/PAR-04-2016-0044.

Weisbach, S., (1988). “Outside directors and CEO turnover”. Journal of Financial Economics, Vol. 20, 431–460.

Whiting, D., G., Hansen, J. V., Mcdonald, J., B., Albrecht, C., & Albrecht, W. S. (2012). Machine learning methods for detecting patterns of management fraud. Computational Intelligence, 28, 505-527.

Witlox, F., Tindemans, H. (2004). "The application of rough sets analysis in activity-based modeling, opportunities and constraints", Expert Systems with Applications, vol. 27, no. 4, pp585-592.

Yeh, C.C.; Chi, D.J.; Lin, Y.R. Going-concern prediction using hybrid random forests and rough set approach. Inf. Sci. 2014, 254, 98–110.

Zhu, J. and Gao, S. S. (2011), “Fraudulent Financial Reporting: Corporate Behavior of Chinese Listed Companies”, Research in Accounting in Emerging Economies, Vol.11, pp. 61-82.