Predicting financial statement fraud using fuzzy neural networks
Subject Areas : 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
Keywords:
Abstract :
[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.