The efficiency of statistical and machine learning models in fraud financial statement

Subject Areas : Financial Economics

حسن ملکی کاکلر ¹ , جمال بحری ثالث ² , سعید جبارزاده کنگرلویی ³ , علی آشتاب ⁴

1 - گروه حسابداری، دانشگاه آزاد اسلامی، واحد ارومیه، ، ارومیه، ایران
2 - گروه حسابداری، دانشگاه آزاد اسلامی واحد ارومیه، ارومیه، ایران
3 - گروه حسابداری، واحد ارومیه، دانشگاه آزاد اسلامی،آذربایجان غربی ،ایران
4 - گروه حسابداری، دانشگاه ارومیه، ارومیه، ایران،

Received: 2021-01-03 Accepted : 2021-03-06 Published : 2021-05-22

Keywords: G32, JEL Classification: M41, M42,

Abstract :

The existence and persistence of fraud in financial statements can have adverse impact on the sustainable development of the capital markets as well as the financial health of companies. Using conventional audit procedures which is applied to prevent and detect fraudulent financial statements, auditors fail to cope with emerging accounting frauds. This can be due to many reasons, such as the lack of the required data mining knowledge, the complexity and infrequency of financial frauds, and the auditors without much experience. Accordingly, due to importance of identifying fraud in capital market, different types of statistical and machine learning based models were examined to establish a rigorous and effective model to detect financial statements fraud in this study. For this purpose, 20 variables in the form of the pentagonal fraud with emphasis on the structure of internal controls (pressure, opportunity, justification, capability, arrogance and internal control structure) were used from 166 manufacturing companies listed on Tehran stock exchange over the period 2009-2018. Based on the statistical indices obtained, machine learning based models exhibited higher predictive ability and accuracy than statistical based models in predicting financial statement fraud. The results also showed that C5, CHAID and C&R decision tree models were highly accurate in prediction of fraudulent data presented in fnancial statement. Accordingly, the efficacy of combination of CHAID, C5 and C&R decision tree algorithms which had the highest accuracy in prediction of fraudulent financial reporting was examined. The high accuracy of 92.61% of the combination of these algorithms in fraud prediction shows that data mining methods based on machine learning models and especially their combination can be used successfully in fnancial statement fraud prediction.

References:

• آشتاب علی, حقیقت حمیدو کردستانی غلامرضا. (1396) "مقایسه ی دقت مدل های پیش بینی بحران مالی و تأثیر آن بر ابزارهای مدیریت سود." بررسی­های حسابداری و حسابرسی، دوره 14 شماره 2 صفحه 172-147


• اعتمادیحسین و زلقیحسن (1392). کاربردرگرسیونلجستیکدرشناساییگزارشگریمالیمتقلبانه. دانشحسابرسی، دورهچهارده،شماره 51 ; 23 - 5


• تاراسی, بنیطالبیدهکردی وزمانی. (1398). پیشبینیگزارشگریمالیمتقلبانهازطریقشبکهعصبیمصنوعی (ANN). حسابداریمدیریت، دوره 12 شماره 40 صفحه 63-79


• خواجوی شکراله، ابراهیمی مهرداد. (1396). مدل‌سازی متغیرهای اثرگذار برکشف تقلب در صورت‌های مالی با استفاده از تکنیک‌های داده‌کاوی . فصلنامه حسابداری مالی. ۱۳۹۶; ۹ (۳۳) :۲۳-۵۰


• قاضی زاده احسایی, نفیسه؛ محمدرضا عباس زاده؛ مهدی صالحی و مهدی جباری نوقابی، (۱۳۹۸) مقایسه دقت فناوری های نوین مدل های آماری و مدل های یادگیری ماشین به منظور پیش بینی ورشکستگی بانک ها، هفدهمین همایش ملی حسابداری ایران، قم، پردیس فارابی دانشگاه تهران.


• صالحی مهدیوفرخیپیلهرود لاله. (1397). پیش بینی مدیریت سود با استفاده از شبکه عصبی و درخت تصمیم. پژوهشهای حسابداری مالی وحسابرسی, دوره 10،شماره 37 صفحه 1-24


• کاردان، قره خانی، صالحی و منصوری. (1396). بررسی دقت الگوریتم های خطی-تکاملی BBO و ICDE و الگوریتم های غیرخطی SVR و CART در پیش بینی مدیریت سود. پژوهش های حسابداری مالی، دوره 31 شماره 1 صفحه 95-77.


• کرمی غلامرضا، داداشی ایمان، فیروزنیا امیر، کلهرنیا حمید. (1398). بررسی تأثیر کیفیت کنترل­های داخلی بر حداقل سازی مالیات در شرکت­های پذیرفته شده در بورس اوراق بهادار تهران در طی سالهای ۱۳۹۴-۱۳۸۸. دانش حسابرسی. دوره ۱8 شماره 72 صفحه 23-55.


• Abtahi, Amir-Reza, Fatemeh Elahi, and Reza Yousefi-Zenouz. "An intelligent system for fraud detection in coin futures market’s transactions of Iran mercantile exchange based on Bayesian network." Journal of Information Technology Management 9, no. 1 (2017): 1-20


• Amaliah, B. N., Januarsi, Y., & Ibrani, E. Y. (2015). Perspektif Fraud Diamond Theory dalam Menjelaskan EarningsManagement Non-GAAP pada Perusahaan Terpublikasi di Indonesia. Jurnal Akuntansi dan Auditing Indonesia, 19(1),51-67.
  Annisya, M., Lindrianasari.,& Asmaranti, Y. (2016). Pendeteksian Kecurangan Laporan Keuangan Menggunakan Fraudو Jurnal Bisnis dan Ekonomi, 23(1), 72-89.


• Apparao, G., Arun Singh, G. S. Rao, B. Lalitha Bhavani, K. Eswar, and D. Rajani. "Financial statement fraud detection by data mining." Corporate governance 3, no. 1 (2009): 159-163.


• Chui, Lawrence, and Byron Pike. "Auditors' responsibility for fraud detection: New wine in old bottles?." Journal of Forensic and Investigative Accounting (2013).


• Chandola, Varun, Arindam Banerjee, and Vipin Kumar. "Anomaly detection: A survey." ACM computing surveys (CSUR) 41, no. 3 (2009): 1-58.


• Chen, Hsinchun, and Mihail C. Roco. "Mapping Nanotechnology Knowledge Via Literature Database: A Longitudinal Study, 1976-2004." In Mapping Nanotechnology Innovations and Knowledge, pp. 1-22. Springer, Boston, MA, 2009.


• Cecchini, Mark, Haldun Aytug, Gary J. Koehler, and Praveen Pathak. "Detecting management fraud in public companies." Management Science 56, no. 7 (2010): 1146-1160.


• Dechow, P. M., & Skinner, D. J. (2000). Earning Mangement: Reconcillling the review of accounting Academics, Practitioners,and Reguators. Accounting Horizontal, 14(2), 235-250


• Esfahanipour, Akbar, Milad Goodarzi, and Reza Jahanbin. "Analysis and forecasting of IPO underpricing." Neural Computing and Applications 27, no. 3 (2016): 651-658.


• Hogan, Chris E., Zabihollah Rezaee, Richard A. Riley Jr, and Uma K. Velury. "Financial statement fraud: Insights from the academic literature." Auditing: A Journal of Practice & Theory 27, no. 2 (2008): 231-252.


• Indarto, S. L., & Ghozali, I. (2016). Fraud Diamond: Detection Analysis on The Fraudulent Financial Reporting. RiskGovernance & Control: Financial Markets & Institution, 6(4), 116-123.


• Khajavi S, Ebrahimi M. Modelling The Effective Variables for of Financial Statements Fraud Detection using Data Mining Techniques .quarterly financial accounting journal. 2017; 9 (33) :23-50


• Lin, Chi-Chen, An-An Chiu, Shaio Yan Huang, and David C. Yen. "Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments." Knowledge-Based Systems 89 (2015): 459-470.


• Min, Jae H., and Young-Chan Lee. "Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters." Expert systems with applications 28, no. 4 (2005): 603-614.


• Omidi, Mahdi, Qingfei Min, Vahab Moradinaftchali, and Muhammad Piri. "The Efficacy of Predictive Methods in Financial Statement Fraud." Discrete Dynamics in Nature and Society


• Sabau, Andrei Sorin. "Survey of clustering based financial fraud detection research." Informatica Economica 16, no. 1 (2012): 110.


• Sharma, Anuj, and Prabin Kumar Panigrahi. "A review of financial accounting fraud detection based on data mining techniques." arXiv preprint arXiv:1309.3944 (2013).


• Jan, Chyan-long. "An effective financial statements fraud detection model for the sustainable development of financial markets: Evidence from Taiwan." Sustainability 10, no. 2 (2018): 513.


• Zhou, Wei, and Gaurav Kapoor. "Detecting evolutionary financial statement fraud." Decision support systems 50, no. 3 (2011): 570-575.


• Zhang, Dongsong, and Lina Zhou. "Discovering golden nuggets: data mining in financial application." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 34, no. 4 (2004): 513-522.


• Akbar, T. (2017). The determination of fraudulent financial reporting causes by using pentagon theory on manufacturing companies in indonesia. International Journal of Business, Economics and Law, 14(5), 106-133.


• Lowensohn, S., and Reck, J., (2004). Longitudinal analysis of local government audit
  Research in Governmental and NonProfit Accounting, 11, 213–228.


• Marsellisa, N (2018). Financial Statement Fraud: Perspective of the Pentagon Fraud Model in Indonesia. Academy of Accounting and Financial Studies Journal.


• O’Keefe, T.B., King, R.D., and Gaver, K.M., (1994). Audit fees, industry specialization,and compliance with GAAS reporting standards. Auditing: A Journal of Practice andTheory, 13(2),41–55.


• Yusof, M.K., Ahmad, K.A.H. &Simon, J. (2015). Fraudulent Financial Reporting: An Application of Fraud Models toMalaysian Public Listed Companies. The Macrotheme Review, 4(3), 126–145.


• Tiffani, L., & Marfuah. (2015). Deteksi Financial Statement Fraud dengan Analisis Fraud Triangle pada Perusahaan Manufaktur
  yang Terdaftar di Bursa Efek Indonesia. Jurnal Akuntansi dan Auditing Indonesia, 19(2), 112-125.


• Tessa, C. G., & Harto, P. (2016). Fraudulent Financial Reporting: Pengujian Teori Fraud Pentagon Pada Sektor Keuangan danPerbankan di Indonesia. Paper presented at Simposium Nasional Akuntansi XIX, held at Universitas Lampung,Lampung, 24-27 Agustus (1-21).

 

 

 

 

 

 

 

 

یادداشت‌ها