ارائة یک رویکرد محاسباتی نوین برای پیش‌بینی تقلب در صورت‌های مالی، با استفاده از شیوه‌های خوشه‌بندی و طبقه‌بندی (شواهدی از شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه آموزشی حسابداری

2 گروه حسابداری

چکیده

رسوایی‌ها و شکست‌های شرکتی، اطمینان سرمایه‌گذاران به درست و منصفانه بودن حساب‌های واحدهای تجاری را مخدوش کرده است. تکنولوژی‌های مبتنی بر آمار و یادگیری ماشینی راهکاری اثربخش برای پیشگیری و کشف تقلب هستند؛ بنابراین در این پژوهش به بررسی این مسئله پرداخته می‌شود که آیا می‌توان از طریق شناسایی عوامل مرتبط با تقلب در صورت‌های مالی و با به‌کارگیری شیوه‌های داده‌کاوی، مدلی برای کشف تقلب در صورت‌های مالی شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران ارائه کرد؟ برای پاسخ‌گویی به این سؤال از 19 علائم خطرِ اشاره‌شده در استاندارد حسابرسی 240 به همراه شیوه‌های داده‌کاوی تحلیل مؤلفه‌های اساسی و خوشه‌بندی، برای تعیین شرکت‌های متقلب استفاده شد؛ سپس به‌منظور ارائة مدلی برای پیش‌بینی صورت‌های مالی متقلبانه، از 40 متغیر مالی و غیرمالی به همراه شیوه‌های درخت تصمیم، ماشین بردار پشتیبان و روش بوستینگ استفاده شد. یافته‌های پژوهش بیان‌گر وجود شواهدی دال بر عملکرد مناسب مدل‌های پیشنهادی برای پیش‌بینی تقلب در صورت‌های مالی است.

کلیدواژه‌ها

عنوان مقاله [English]

A Novel Computational Approach to Predict Financial Statements Fraud using Clustering and Classification Techniques: Evidence from Listed Companies in Tehran Stock Exchange

نویسندگان [English]

  • Shokrolah Khajavi 1
  • Mehrdad Ebrahimi 2
1
2
چکیده [English]

  Introduction
Recent scandals and corporate failures have shaken the confidence of investors as accounts which were purported to reflect a “true and fair view” of businesses have been misleading. In an era with evolutionary financial frauds, computer assisted automated fraud detection mechanisms will be more effective and efficient with specialized domain knowledge. Statistics and machine learning based technologies have been shown to be an effective way to deter and defect fraud. Therefore, this study deals with the identification of factors related to fraudulent financial statements and investigating the effectiveness of data mining techniques in detecting firms that issue fraudulent financial statements.
 
Research Questions
In order to achieve the objectives of this research, the following questions are developed:
1. Can the fraudulent and non-fraudulent financial statements be detected from the text of annual corporate audit report fillings?
2. Can a quantitative fraud detection model be developed that will provide an analytical procedure for automating detection of potential fraud?
 
Methods
Data mining is used in many domains, including finance, engineering and biomedicine. There are two categories of data mining methods: unsupervised and supervised. Identifying financial statements fraud can be regarded as a typical classification problem. Data mining proposes several classification methods derived from the fields of statistics and artificial intelligence. Three methods, which enjoy a good reputation for their classification capabilities, are employed in this research study. These methods are Decision Trees, Support Vector Machines and Boosting algorithm. In this research, nineteen red flags of fraud extracted from auditing standard No. 240 along with data mining techniques such as principal components analysis and modified K-Means clustering were used to discriminate financial statements fraud cases. We consider data mining based financial fraud detection techniques such as Decision Trees, Support Vector Machines and Boosting approach in order to identify fraud. Based upon the fraudulent financial reporting related literature, 40 predictor variables are selected as input variables. These variables represent measurement proxies for a firm’s attributes of financial leverage, profitability, asset composition, liquidity, efficiency, size, growth, overall financial position, audit firm size, audit firm tenure and auditor change. These predictor variables are collected from the financial statements of listed companies in Tehran Stock Exchange (TSE) for the period of 2003-2015.
 
Results
This research uses a quantitative approach on textual data to discriminate fraud cases using text mining techniques. The first research question was: can the fraudulent and non-fraudulent financial statements be detected from the text of annual corporate audit report fillings? Modified k-Means clustering demonstrated a good ability to discriminate fraud cases from non-fraud cases and thus it answered the first research question. the successful implementation of the Decision Trees, Support Vector Machines and Boosting algorithms also answers the second research question: can a quantitative fraud detection model be developed that will provide an analytical procedure for automating detection of potential fraud? In comparative assessment of the models’ performance, Support Vector Machines achieved the best performance to correctly classify validation sample in a 5-fold cross validation procedure. Decision trees and boosting algorithms also achieve a satisfactorily high performance.
 
Discussion and Conclusion
The present study contributes to auditing and accounting research by examining the suggested variables that can best discriminate cases of financial statements fraud. These results suggest that there is validity for detecting financial statement fraud using data mining techniques such as Decision Trees, Support Vector Machines and Boosting approach. Our analysis provides insight for auditors, taxation authorities, investors, the stock exchange and banking system.
 
Keywords: Fraud detection, Financial statements fraud, Data mining, Tehran stock exchange.
 
 

مراجع

اعتمادی، حسین و زلقی، حسن (1392). کاربرد رگرسیون لجستیک در شناسایی گزارشگری مالی متقلبانه. دانش حسابرسی، 13(51)، 145-163.
پیری، پرویز و برزگری صدقیانی، سمانه (1395). بررسی رابطة بین دورة تصدی حسابرس و تغییر حسابرس با کیفیت سود شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران بر اساس مدل کاسنیک. پیشرفت‌های حسابداری، 8(2)، 65-91.
جهانشاد، آزیتا و سرداری‌زاده، سپیده (1393). رابطة معیار مالی (اختلاف رشد درآمد) و معیار غیرمالی (رشد تعداد کارکنان) با گزارشگری مالی متقلبانه. پژوهش حسابداری، 4(13)، 181-198.
خواجوی، شکراله و قدیریان آرانی، محمدحسین (1394). بررسی تأثیر کیفیت سود بر تجدید ارائة صورت‌های مالی. پیشرفت‌های حسابداری، 7(2)، 59-84.
صفرزاده، محمدحسین (1389). توانایی نسبت‌های مالی در کشف تقلب در گزارشگری مالی: تحلیل لاجیت. دانش حسابداری، 1(1)، 137-163.
فرقاندوست حقیقی، کامبیز؛ هاشمی، سید عباس و فروغی دهکردی، امین (1393). مطالعة رابطة بین مدیریت سود و امکان تقلب در صورت‌های مالی شرکت‌های پذیرفته‌شده در ورس اوراق بهادار تهران. دانش حسابرسی، 14(56)، 47-68.
کمیتة تدوین استانداردهای حسابرسی (1394). اصول و ضوابط حسابداری و حسابرسی: استانداردهای حسابرسی. چاپ بیست و پنجم، تهران، سازمان حسابرسی.
مرادی، جواد؛ رستمی، راحله و 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. پیشرفت‌های حسابداری، 6(1)، 141-173.
Alden, M. E., Bryan, D, M., Lessley, B., J., & Tripathy, A. (2012). Detection of financial statement fraud using evolutionary algorithms. Journal of Emerging Technologies in Accounting, 9, 71-94.
Altman, E. I., (1968). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. Journal of Finance, 23, 589-609.
Association of Certified Fraud Examiners (ACFE) (2011). Fraud examiners manual. Austin, Texas: ACFE.
Association of Certified Fraud Examiners (ACFE) (2012). Report to the nations on occupational fraud and abuse. Austin, Texas: ACFE.
Audit Standards Development Committee. (2015). Principles and criteria of accounting and auditing: auditing standards. Twenty-Fifth Edition, Tehran, Auditing Organization. (In Persian)
Brazel, J. F., Jones, K., L., & Zimbelman, M. F. (2009). Using nonfinancial measures to assess fraud risk. Journal of Accounting Research, 47, 1135-1166.
Carcello, J. V., & Nagy, A. L. (2004). Audit firm tenure and fraudulent financial Feporting. Auditing: A Journal of Practice & Theory, 23, 55-69.
Chen, F. H., Chi, D. J., & Zhu, J. Y. (2014). Application of random forest, rough set theory, decision tree and neural network to detect financial statement fraud–Taking torporate governance into consideration. Intelligent Computing Theory, 8588, 221-234.
Cort´es, E. A., Mart´ınez, M. G., & Rubio, N. G. (2007). A boosting approach for corporate failure prediction. Applied Intelligence, 27, 29-37.
Dua, S., & Du, X. (2011). Data Mining and Machine Learning in Cybersecurity. 1st edition. Milton Park, UK: Taylor and Francis Group.
Etemadi, H., & Zalqi, H. (2013). Application of logistic regression in identifying fraudulent financial reporting. Auditing Knowledge, 13(51), 145-163. (In Persian)
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861-874.
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.
Forghandoste Haghighi, K., Hashemi, S. A., & Foroughi Dehkordi, A. (2014). A study of the relationship between earnings management and the possibility of fraud in the financial statements of companies listed on the Tehran Stock Exchange. Auditing Knowledge, 14(56), 47-68. (In Persian)
Gepp, A., Kumar. K., & Bhattacharya, S. (2010). Business failure prediction using decision trees. Journal of Forecasting, 29, 536-555.
Han, J., Kamber, M., & Pey, J. (2012). Data Mining Concepts and Techniques. 3rd Edition, Morgan Kaufmann Publications.
He, H., Bai, Y., Garcia, E, A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. IEEE Neural Networks, 1322-1328.
Hoogs, B., Kiehl, T., Lacomb, C., & Senturk, D. (2007). A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud. International Journal of Intelligent Systems in Accounting, Finance and Management, 15, 41-56.
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.
Jahanshad, A., & Sardari Zadeh, S. (2014). Relation between difference of financial measure (revenue growth) and nonfinancial measure (employee growth) with fraudulent financial reporting. Journal of Accounting Research, 4(2), 181-198. (In Persian)
Kaminski, K. A., Wetzel, T. S., & Guan, L. (2004). Can financial ratios detect fraudulent financial reporting? Managerial Auditing Journal, 19,15–28.
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization, 39, 459-471.
Khajavi, S., & Ghadirian Arani, M. (2016). Investigation of the impact of earnings quality on restatement of financial statements. Journal of Accounting Advances, 7(2), 59-84. (In Persian)
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32, 995–1003.
Lin, C-H., Chiu, A-A., Huang, S. Y., & Yen, D. C. (2016). Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments. Knowledge-Based Systems, 89, 459-470.
Moradi, J., Rostami, R., & Zare, R. (2014). Recognizing risk factors affecting fraud probability in financial reporting from auditors' viewpoint and its impact on firms' performance. Journal of Accounting Advances, 6(1), 141-173. (In Persian)
Persons, O. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research, 11, 38–46.
Pierre, K. S., & Anderson, J. A. (1984). An analysis of the factors associated with lawsuits against public accountants. The Accounting Review, 59, 242-263.
Piri, P., & Barzegari Sadaghiani, S. (2017). A Study of the relation between audit firm rotation and audit firm tenure on income quality at Tehran Stock Exchange Companies based on Kasznik model. Journal of Accounting Advances, 8(2), 65-91. (In Persian)
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.
Rezaee, Z. (2005). Causes, consequences, and deterrence of financial statement fraud. Critical Perspective on Accounting, 16, 277-298.
Rezaee, Z., & Riley, R. (2010). Financial Statement Fraud-prevention and Detection. 2nd edition, John Wiley & Sons, Inc.
Safarzadeh, M. (2012). The ability of financial ratios in detecting fradulent financial reporting: logit analysis.Journal of Accounting Knowledge, 1(1), 137-163. (In Persian)
Shin, K. S., Lee, T. S., & Kim, H. J. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28, 127-135.
Spathis, C. T. (2002). Detecting false financial statements using published data: Some evidence from Greece. Managerial Auditing Journal, 17, 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.
Sreejesh, S., Anusree, M. R., & Mohapatra, S. (2014). Business Research Methods: An Applied Orientation. 1st edition, Springer International Publishing.
Stice, D. J. (1991) Using financial and market information to identify pre-engagement market factors associated with lawsuits against auditors. Accounting Review, 66, 516–33.
Summers, S. L., & Sweeney, J. T. (1998). Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 73, 131–46.
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.
Zhou, W., & Kapoor, G. (2011). Detecting evolutionary financial statement fraud. Decision Support Systems, 50, 570-575.
Zikmund, W. G., Babin, B. J., Car, J. C., & Griffin, M. (2010). Business Research Methods. 8th edition, Cengage Learning.
 
 

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  • تاریخ دریافت: 15 مرداد 1395
  • تاریخ بازنگری: 22 فروردین 1397
  • تاریخ پذیرش: 02 اردیبهشت 1396

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