Predicting financial statement fraud using fuzzy neural networks
محورهای موضوعی : Financial MathematicsMohsen Rostamy-Malkhalifeh 1 , Maryam Amiri 2 , Mehrdad Mehrkam 3
1 - Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Management and Economics, Science And Research Branch, Islamic Azad University
,Tehran,Iran
3 - Department of Management and accounting, Allameh Tabataba’i University, Tehran, Iran
کلید واژه: fuzzy neural network, Financial statement, Fraud,
چکیده مقاله :
Fraud is a common phenomenon in business, and according to Section 24 of the Iranian Auditing Standards, it is the fraudulent act of one or more managers, employees, or third parties to derive unfair advantage and any intentional or unlawful conduct. Financial statements are a means of transmitting confidential management information about the financial position of a company to shareholders and other stakeholders. In this paper, by reviewing the literature, 6 indicators of current ratio, debt ratio, inventory turnover ratio, sales growth index, total asset turnover ratio, and capital return ratio as input and detection of financial fraud as output are considered for the fuzzy neural network. The database was compiled for 10 companies in the period from 2010 to 2018 after clearing and normalizing qualitatively between 1 to 5 discrete numbers with very low or very high meanings, respectively. The fuzzy neural network model with 161 nodes, 448 linear parameters, 36 nonlinear parameters, and 64 fuzzy laws with two methods of accuracy approximation of mean squared error and root mean squared error has been set to zero and 0.0000001 respectively. This neural network can be used for prediction.
[1] Rahnamay, R.F., Data mining & financial fraud. Journal of Accounting Knowledge and Management Auditing, 2012. 1(3): p. 17-33.
[2] Azarnioush, S.I., B.S. Dailagh, and H. Mardani, Pridicting Financial statement Risk Fraud Using Artificial Neural Networks, in First International Conference on Accounting, Management, and Business Innovation. 2015: Gilan, Iran.
[3] Izadikhah, M. and A.J.R.-O.R. Khoshroo, Energy management in crop production using a novel fuzzy data envelopment analysis model. 2018. 52(2): p. 595-617.
[4] Izadikhah, M., A fuzzy goal programming based procedure for Machine Tool Selection. Journal of Intelligent & Fuzzy Systems, 2015. 28(1): p. 361-372.
[5] Halim, B.A., et al., Bank financial statement management using a goal programming model. Procedia-social and behavioral sciences, 2015. 211: p. 498-504.
[6] Jiawei Han, M. and J. Pei, Data mining: concepts and techniques: concepts and techniques. 2011, Elsevier, Amsterdam.
[7] Moslemzadeh, A., Data Mining Methods to Detect Fraud in Financial Statements Auditing. Journal of Official Accountants, 2011.
[8] Krambia Kapardis, M., C. Christodoulou, and M. Agathocleous, Neural networks: the panacea in fraud detection? Managerial Auditing Journal, 2010. 25(7): p. 659-678.
[9] Jameie, R. and P. Asgharzadih, Reviews The Performance Gap Fraud Field Of View And Use Independent Auditors Auditing Services, in The First Regional Conference On New Approaches Accounting And Auditing. 2012.: Bandar Gaz, Azad University Of Bandar Gaz Branch.
[10] Rahimian, N. and M. Akhundzade, Role of Internal Audit in Preventing and Detecting Fraud. Journal of Certified Public Accountants, 2011. 15: p. 40-44.
[11] Patel, H., et al., An application of ensemble random forest classifier for detecting financial statement manipulation of Indian listed companies, in Recent Developments in Machine Learning and Data Analytics. 2019, Springer. p. 349-360.
[12] Lin, C.-C., et al., Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments. Knowledge-Based Systems, 2015. 89: p. 459-470.
[13] Mofarreh, M., Using Data Mining Techniques to Detect Fraud in Financial Accounts, in Second International Conference on Research in Engineering, Science and Technology. 2015.
[14] Abouzari Khoie, N. and A. Hatamlou, Application of Cuckoo Optimization Algorithm in Solving Different Optimization Problems, National Conference on Computer and Information Technology Management. 2015.
[15] Sharma, A. and P.K. Panigrahi, A review of financial accounting fraud detection based on data mining techniques. arXiv preprint arXiv:1309.3944, 2013.
[16] Ghalami, Z.Z. Using Data Mining to Detect Credit Card Transaction Fraud. in Proceedings of the Second National Conference on New Technologies in Electrical and Computer Engineering. 2014. Islamic Azad University of Fasa.
[17] Sabzi Parvar, A.A. and M. Bayat Varkeshi, evaluating the accuracy of artificial neural and fuzzy neural network methods in simulating total solar radiation. 2010.
[18] Khanna, T., Foundations of neural networks. Reading: Addison Wesley, 1990, 1990.
[19] Dayhoff, J., Neural network principles. PrenticeHall International, USA, 1990.
[20] Nourani, V. and K. Salehi, in 4th National Congress of Civil Engineering. 2008.: University of Tehran.
[21] UĞURLU, M. and Ş. SEVİM, Artificial neural network methodology in fraud risk prediction on financial statements; an emprical study in banking sector. Journal of Business Research-Turk, 2015. 7(1): p. 60-89.
[22] Sorkun, M.C. and T. Toraman, Fraud Detection on Financial Statements Using Data Mining Techniques. Intelligent Systems and Applications in Engineering, 2017. 5(3): p. 132-134.
