New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony

Document Type : Research Paper

Authors

1 Ph.D in Accounting, Tarbiat Modares University, Tehran, Iran.

2 Associate Professor of Accounting, Tarbiat Modares University, Tehran, Iran.

3 Professor of Accounting, Tarbiat Modares University, Tehran, Iran.

10.22103/jak.2019.13616.2927

Abstract

Objective: Considering complex financial plans to conceal fraud in financial statements, the development of fraud detection methods can be regarded as solution for this problem. The present study uses the bee algorithm to develop methods for fraud detection in financial statements.
Method: Three methods of bee algorithm, genetic algorithm and logistic regression have been used to study the subject. The statistical sample consists of 120 companies accepted in the Tehran Stock Exchange (60 companies are suspected of fraud and 60 ones are not suspected) for the period 1396-1385. The companies were suspected of fraud, based on 1) revised audit opinion after unacceptable expression, 2) existence of significant annual revisions, and revised financial statements for inventories and other assets; 3) existence of tax disputes with the tax area, according to notes on income tax filing, general tax filings and conditioned clauses in audit reports. Following the use of cross-entropy, 16 financial ratios were introduced as the potential predictors of fraudulent financial reporting.
Result: The results showed that the bee algorithm method with prediction accuracy of 82.5% has better performance in identifying suspicious companies in fraudulent financial statements than the other two methods.
Conclusion: The results of the research indicate that the proposed method of this study compared to other methods has higher rate of prediction accuracy, lower error rate and relatively good speed.

Keywords

References

اعتمادی، حسین؛ زلقی، حسن. (1392). کاربرد رگرسیون لجستیک درشناسایی گزارشگری مالی متقلبانه. دانش حسابرسی، 51، 25-1.
تقوی، مصطفی ؛ نوبری، نازک. (1385). کاربرد الگوریتم‌های تکاملی در داده کاوی. اولین همایش بین المللی روش‌های تحقیق در فنون مهندسی، 15-23.
خواجوی، شکراله؛ ابراهیمی، مهرداد. (1396). مدلسازی متغیرهای اثرگذار برای کشف تقلب در صورت‌های مالی با استفاده از تکنیک داده کاوی، حسابداری مالی،33، 25-24.
رحیمی، امیرمسعود؛ رمضانی خوانساری، احسان. (1393). توسعۀ الگوریتم غذایابی کندوی زنبور عسل برای حل مسئله مسیریابی خودرو، مهندسی حمل و نقل، 6(1)، 115-102.
صدیقی کمال، لیلا. (1392). تقلب در صورت‌های مالی براساس گزارش انجمن بازرسان رسمی تقلب، حسابرس، 64، 118-116.
صفرزاده، محمدحسین. (1389). توانایی نسبت‌های مالی در کشف تقلب در گزارشگری مالی:تحلیل لاجیت، دانش حسابداری، 1(1)،163-137.
قادری، کاوه؛ قادری، صلاح‌الدین. (1396). تحلیل بیش‌اطمینانی مدیران از عملکرد خود در شرکت‌های متقلب، بررسی‌های حسابداری و حسابرسی، 2(24)، 262-243.
فرقاندوست حقیقی، کامبیز؛ هاشمی، سیدعباس؛ فروغی دهکردی، امین. (1393). مطالعۀ رابطۀ مدیریت سود و امکان تقلب در صورت‌های مالی شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران، دانش و پژوهش حسابداری، 56، 68-47.
کمیته تدوین استانداردهای حسابرسی. (1384). استانداردهای حسابرسی، سایر خدمات اطمینان بخشی و خدمات مرتبط، سازمان حسابرسی.
گرجی، عطااله. (1388). بررسی موانع به کارگیری الگوریتم ژنتیک در انتخاب سبد سرمایه‌گذاری توسط سرمایه‌گذاران در بورس اوراق بهادار تهران، پایان‌نامۀ کارشناسی ارشد، دانشگاه آزاد اسلامی واحد تهران مرکزی.
مرادی، جواد؛ رستمی، راحله؛ زارع، رضا. (1393). شناسایی عوامل خطر مؤثر بر احتمال وقوع تقلب در گزارشگری مالی از دید حسابرسان و بررسی تأثیر آنها بر عملکرد مالی شرکت، مجله پیشرفت‌های، 1، 173-141.
مهدوی، غلامحسین؛ قهرمانی، علیرضا. (1396). ارائۀ الگویی برای کشف تقلب به وسیله حسابرسان با استفاده از شبکۀ عصبی مصنوعی، دانش حسابرسی، 67، 60-50.
هاشمی، سیدعباس؛ حریری، امیرسینا. (1396). ارزیابی توانایی قانون بنفورد در شناسایی و پیش‌بینی کشف تقلب مالی، بررسی‌های حسابداری و حسابرسی، 2(24)، 302-283.
References
Abdul Aris, N., Mohd A., Siti Maznah, O.R., Mohamad Zain, M. (2015). Fraudulent Financial statement detection using statistical techniques: the case of small medium automotive enterprise. The Journal of Applied Business Research, 31(4), 115-120.
Association of Certified Fraud Examiners (ACFE). (2012). Gobal Fraud Study.
Auditing Standards Setting Committee (2005). Standard on Auditing, Other Assurance Engagmens, and Related Services, Audit Organization [In Persian].
Blakley, M. (2009). Fraud detection using a database platform. Available online at: http://www.slideshare.net/mblakley, 24 April 2013.
Dorminey, J.W., Fleming, A.S., Kranacher, M., Riley, R.A. (2012). Financial fraud: A new prespective to an old problem. The CPA Journal, 8(3), 61-65.
El-Dyasty, M. (2002). Combining belief functions and neural networks to assess the likelihood of fraud: The case of commercial bank audits, Working Paper, Manscmra University, 1-45.
Etemadi, H., Zelaghi, H. (2014). Application of logistic regression in identifying fraud financial reporting. Auditing Knowledge, 51, 1-25 [In Persian].
Forghandust Haghighi, K., Hashemi, S.A., Foroughi Dehkordi, A. (2014). Studying the relationship between earnings management and the possibility of fraud in the financial statements of listed companies in Tehran Stock Exchange. Knowledge and Research Accounting, 47, 56-68 [In Persian].
Ghaderi, K., Ghadei, S. (2017). The analysis of the executive overconfidence in fraudulent companies. Journal of Accounting and Auditing Review, 24(2), 243-262 [In Persian].
Gorji, A. (2009). Investigating the Obstacles Using Genetic Algorithm in Selecting Investment Basis by Investors in Tehran Stock Exchange, Master's Thesis, Islamic Azad University, Tehran Central Branch [In Persian].
Guney, K., Onay, M. (2010). Bees algorithm for interference suppression of linear antenna arrays by controlling the phase-only and both the amplitude and phase. Expert Systems with Applications, 37, 3129–3135.
Hashemi, S.A., Hariri, A.S. (2017). The analysis of bedford’s law ability to identify and predict financial fraud detection., Journal of Accounting and Auditing Review, 24(2), 283-302 [In Persian].
Kotsiantis, S. (2007). Forecasting fraudulent financial statements using data mining. World Enformatika Soc., 12, 283-288.
Karaboga, D., Celal, O. (2011). A novel clustering approach: Artificial bee colony (ABC) algorithm, Applied Soft Computing, 11, 652- 657.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, Technical Report, Computer Engineering Department, Engineering Faculty, Erciyes University.
Khajavi, S., Rezaei, G. (2017). Modeling effective variables for fraud detection in financial statements using data mining techniques. Journal of Financial Accounting, 33, 24-26 [In Persian].
Mahdavi, G.H., Ghahremani, A. (2017). Providing a model for fraud investigation by auditors using artificial neural network. Journal of Audit Science, 67, 50-60 [In Persian].
Moradi, J., Rostami, R., Zare, R. (2014). Identifying the risk factors affecting the likelihood of fraud in financial reporting from the auditor's point of view and investigating their impact on financial performance. Journal of Accounting Advancement, 1, 141-173 [In Persian].
Peng, H., Long, F., Ding, C. (2005). Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Machine Intell, 6, 26-38.
Priya Ranganathan, C.S., Pramesh, L., Rakesh A. (2017). Common pitfalls in statistical analysis: Logistic regression. Perspect Clin Res, 8(3), 148–151.
Reddy, Y.V., Sebastian, A. (2009). Parameters for estimation of entropy to study price manipulation in stock markets. Research publication University of Dehli.
Rezaee, Z., Riley, R. (2010). Financial Statement Fraud: Prevention and Detection, 2nd edition. Hoboken New Jersey: John Wiley and Sons Inc. xi, 12-284.
Sadgalia, I., Saela, N., Benabbou, F. (2019). Performance of machine learning techniques in the detection of financial frauds. Second International Conference on Intelligent Computing in Data Sciences (ICDS 2018), 1986, 92-100.
Safarzadeh, M.H. (2010). The ability of financial ratios to detect fraud in financial reporting: logit analysis. Accounting Knowledge, 1(1), 137-163 [In Persian].
Samer, M.S., Sakinah, A.P. (2015). Significance of parameters in genetic algorithm, the strengths, its limitations and challenges in image recovery. Journal of Engineering and Applied Sciences, 10(2), 2001-2010.
Sedighi Kamal, L. (2014). Fraud in financial statements according to the report of the association of official insolvency officers. Auditor, 64, 116-118 [In Persian].
Stice, J. (1991). Using financial and market information to identify preengagement market factors associated with lawsuits against auditors. The Accounting Review, 66(3), 516-533.
Suduan Chen. (2016). Detection of fraudulent financial statements using the hybrid data mining approach. Springer Plus, 5(89), 509-535.
Tangod, K., Kulkarni, G. (2015). Detection of financial statement fraud using data mining technique and performance analysis. International Journal of Advanced Research in Computer and Communication Engineering, 4(7), 312-320.
Journal of Accounting Knowledge
Volume 10, Issue 3 - Serial Number 38
November 2019
Pages 139-167

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History

  • Receive Date: 17 February 2019
  • Revise Date: 15 April 2019
  • Accept Date: 05 May 2019

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