Fraud Detection on Financial Statements Using Data Mining Techniques

This study explores the use of data mining methods to detect fraud for on e-ledgers through financial statements. For this purpose, data set were produced by rule-based control application using 72 sample e-ledger and error percentages were calculated and labeled. The financial statements created from the labeled e-ledgers were trained by different data mining methods on 9 distinguishing features. In the training process, Linear Regression, Artificial Neural Networks, K-Nearest Neighbor algorithm, Support Vector Machine, Decision Stump, M5P Tree, J48 Tree, Random Forest and Decision Table were used. The results obtained are compared and interpreted

PDF

___

[1] Kirkos, E., Spathis, C., and Manolopoulos, Y. (2007). Data Mining Techniques for the Detection of Fraudulent Financial Statements. Expert Systems with Applications Vol. 32.4 Pages.995-1003.

[2] Ravisankar, P., Ravi, V., Rao, G. R., and Bose, I. (2011). Detection of Financial Statement Fraud and Feature Selection Using Data Mining Techniques. Decision Support Systems, Vol. 50, Pages. 491-500.

[3] Association of Certified Fraud Examiners (2016). The Staggering Cost of Fraud [Online]. Available: http://www.acfe.com/rttn2016/docs/Staggering-Cost-ofFraud-infographic.pdf

[4] Kotsiantis, S., Koumanakos, E., Tzelepis, D., and Tampakas, V. (2006). Forecasting Fraudulent Financial Statements Using Data Mining. International Journal of Computational Intelligence, Vol. 3,Pages. 104-110. Asdasdas

[5] Zhou, W., and Kapoor, G. (2011). Detecting Evolutionary Financial Statement Fraud. Decision Support Systems, Vol. 50, Pages. 570-575.

[6] Ata, H. A., and Seyrek, I. H. (2009).The Use of Data Mining Techniques inDetecting Fraudulent Financial Statements: An Application on Manufacturing Firms. The Journal of Faculty and Economics and Administrative Sciences, Vol. 14. Pages.157-170.

[7] Sharma, A., and Panigrahi, P. K. (2013). A review of financial accounting fraud detection based on data mining techniques.International Journal of ComputerApplication, Vol. 39, Pages. 37–47.

[8] Apparao, G., Singh, A., Rao, G. S., Bhavani, B. L., Eswar, K., and Rajani, D. (2009). Financial Statement Fraud Detection by Data Mining. International Journal of AdvancedNetworking and Applications, Vol. 1. Pages. 159- 163.

[9] Hoogs, B., Kiehl, T., Lacomb, C., and Senturk, D. (2007). A Genetic Algorithm Approach to Detecting Temporal Patterns Indicative of Financial Statement Fraud. Intelligent Systems in Accounting, Finance and Management, Vol. 15. Pages. 41-56.

[10] Çömlekçi, F. (2004). Muhasebe Denetimi ve Mali Analiz. Anadolu University Publication.

[11] Özkul, F. U., and Pektekin, P. (2009). Muhasebe Yolsuzluklarının Tespitinde Adli Muhasebecinin Rolü ve Veri Madenciliği Tekniklerinin Kullanılması. World of Accounting Science, Vol. 11.

[12] Turkish Cultural Foundation, Proverbs [Online]. Available: http://www.turkishculture.org/literature/literature/turkishproverbs-133.htm

[13] Denetim İlkeve Esasları (2004). Maliye Hesap Uzmanları Derneği, Vol.1. Page. 151.

[14] Terzi, S., (2012). Hileve Usulsüzlüklerin Tespitinde Veri Madenciliğinin Kullanımı. Journal of Accounting & Finance, Vol. 54. Pages. 51-65