Mali Tablo Denetiminde Ön Analitik İnceleme Tekniği Olarak Veri Madenciliğinin Kullanımı: Borsa İstanbul Uygulaması

Year 2014, Volume: 5 Issue: 1, 13 - 36, 01.06.2014

Hilmi Kırlıoğlu , İsmail Fatih Ceyhan

Abstract

Kaliteli bir denetim gerçekleştirmek adına denetçi mümkün olduğunca denetleyeceği firma hakkında varacağı yargıya kısa bir süre içinde ulaşmalıdır. Bu yargıya varmak için denetçi, firmanın mali tablolarından yararlanmaktadır. Uluslararası Finansal Raporlama Standartlarına uygun olarak düzenlenmiş bu mali tablolar, firma hakkında bilgi sağlayan önemli göstergeler olup, denetim programı aşamalarından olan analitik inceleme faaliyeti sırasında denetçinin yararlanacağı araçlardan biridir. Çalışmamızda Borsa İstanbul’a kayıtlı 40 şirketin mali tablo verileri kullanılmış, bu şirketlerin veri madenciliği algoritmalarıyla finansal olarak başarılı ya da başarısız olarak sınıflandırılarak ön analitik inceleme faaliyetinin kısa sürede tamamlanması hedeflenmiştir. Sonuç olarak, k-en yakın komşu algoritması ve 10 kat çapraz doğrulama tekniği ile %95 gibi yüksek bir oranda doğru finansal sınıflama tahmini elde edilmiştir.

Keywords

Mali Tablo Denetimi, Veri Madenciliği, Sınıflama Analizi, Weka

References

• Aci, M. & Avci, M. (2011). K nearest neighbor reinforced expectation maximization method. Expert Systems with Applications, 38, 10, 15, 12585-12591.
• Akyel, N. & Seçkin K. (2011). K-en yakın komşuluk algoritmasının hile denetiminde kullanımı. Muhasebe ve Vergi Uygulamaları Dergisi, 51, 21-39.
• Albayrak, A.S. & Koltan Yılmaz, Ş. (2009). Veri madenciliği karar ağacı algoritmaları ve İMKB verileri üzerine bir uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14, 1, 31-52.
• Almonacid F., Fernández, E.F., Rodrigo, P., Pérez-Higueras, P.J. & Rus-Casas, C. (2013). Estimating the maximum power of a high concentrator photovoltaic (HCPV) module using an Artificial Neural Network. Energy, 53, 165-172.
• Altman, E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, XXIII, 4, 589-609.
• Arens, A. & Loebbecke, J. (2000). Auditing: An Integrated Approach. New Jersey: Prentice Hall. Arens, A., Randall , J., & Beasley, M. (2006). Auditing and Assurance Services: An Integrated Approach (11. b.). New Jersey: Prentice Hall.
• Bankalarda Bağımsız Denetim Gerçekleştirecek Kuruluşların Yetkilendirilmesi ve Faaliyetleri Hakkında Yönetmelik, md.33/g
• Bhat, M.S. & Rau, A.V. (2008). Managerial Economics and Financial Economics. Hyderabad Sultan Bazar: BS Publications,.
• Boran, L. (2012). Veri Madenciliğinin Türk İşletmelerin Finansal Tablolarına Uygulanması ve Uygulama Örneği. Yayınlanmamış Doktora Tezi, Marmara Üniversitesi S.B.E., İstanbul.
• Cerullo, M. J. & Cerullo, V. (1999). Using neural networks to predict financial reporting fraud. Computer Fraud & Security, May/June, 14–17.
• Cunha C. D., Agard, B., & Kusiak, A. (2006). Data mining for improvement of product quality. International Journal of Production Research, 44:18/19, 4041–4054.
• Fanning, K., Cogger, K., & Srivastava, R. (1995). Detection of management fraud: a neural network approach. International Journal of Intelligent Systemsin Accounting, Finance & Management, 4, 2, 113– 26.
• Fanning, K. & Cogger, K. (1998). Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management, 7, 1, 21- 24.
• Feroz, E. H., Kwon, T. M., Pastena, V. & Park, K. J. (2000). 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, 3, 145–157.
• Fushiki, T. (2011). Estimation of prediction error by using K-fold cross-validation. Stat Comput, 21, 137–146.
• Glancy, F. H. & Surya, B. Y. (2011). A computational model for financial reporting fraud detection. Decision Support Systems, 50, 95–601.
• Gorunescu, F. (2011). Data Mining Concepts,Models and Techniques (12.b.). Romania: Intelligent Systems Reference Library.
• Goyal, S. & Goyal, G. K. (2011). A New Scientific Approach of Intelligent Artificial Neural Network Engineering for Predicting Shelf Life of Milky White Dessert Jeweled with Pistachio. International Journal of Scientific & Engineering Research, 2, 9, 1-4.
• Green, B. P. & Choi, J. H. (1997). Assessing the risk of management fraud through neural-network technology. Auditing: A Journal of Practice and Theory, 16(1), 14–28.
• http://www.cs.waikato.ac.nz/ml/weka/ Erişim Tarihi: 18.09.2013
• http://ecs.victoria.ac.nz/Courses/COMP307_2014T1/Lect13-team1 Erişim Tarihi: 17.04.2014
• http://www.kap.gov.tr/sirketler/islem-goren-sirketler/pazarlar Erişim Tarihi: 11.04.2013
• http://www.stat.gen.tr/index.php?
• istek=sinif&dersid=ist01&konuid=ver01&max=1 Erişim Tarihi: 17.04.2014 http://www.aicpa.org/Research/Standards/AuditAttest/DownloadableDocuments/AU-00329.pdf Erişim Tarihi: 16.06.2013
• http://www.ifac.org/sites/default/files/publications/files/A027%202012%20IAASB%20Handbook%20ISA%205 20.pdf Erişim Tarihi: 18.06.2013
• Han, J., Kamber, M. & Pei J. (2012). Data Mining (Third Edition). Boston: Morgan Kaufmann.
• Huang, S.Y., Tsaih, R. H. & Yu, F. (2014). Topological pattern discovery and feature extraction for fraudulent financial reporting. Expert Systems with Applications, 41, 9, 4360–4372.
• Jans M., Lybaert, N. & Vanhoof, K. (2010). Internal fraud risk reduction: Results of a data mining case study.
• International Journal of Accounting Information Systems, 11, 17–41. Kapardis, M. K., Christodoulou, C. & Agathocleous, M. (2010). Neural networks: the panacea in fraud detection? Managerial Auditing Journal, 25, 659-678.
• Kirkos, E., Spathis, C. & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32, 4, 995–1003.
• Koskivaara, E. (2000). Different pre-processing models for financial accounts when using neural networks for auditing. Proceedings of the 8th European Conference on Information Systems, 1, (pp.326–3328).
• Kotsiantis, S., Koumanakos, E., Tzelepis, D. & Tampakas, V. (2006). Forecasting fraudulent financial statements using data mining. International Journal of Computational Intelligence, 3, 2, 104–110.
• Kou, G., Peng Y. & Guoxun W. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences. http://dx.doi.org/10.1016/j.ins.2014.02.137 Erişim Tarihi: 27.03.2014.
• Kuzey, C., Uyar, A. Delen, & D. (2014). The impact of multinationality on firm value: A comparative analysis of machine learning techniques. Decision Support Systems, 59, 127–142. Larose, D., T. (2005). Discovering Knowledge in Data: An Introduction to Data Mining., Hoboken: John Wiley and Sons.
• Lin, J. W., Hwang, M. I. & Becker, J. D. (2003). A Fuzzy Neural Network for Assessing the Risk of Fraudulent Financial Reporting. Managerial Auditing Journal, 18, 8, 657-665.
• Liou, F. M. (2008). Fraudulent financial reporting detection and business failure prediction models: a comparison. Managerial Auditing Journal, 23, 7, 650-662.
• Mastrogiannis, N., Boutsinas, B. & Giannikos, I. (2009). A method for improving the accuracy of data mining classification algorithms. Computers and Operations Research, 36, 10, 2829-2839.
• Ngai, E.W.T., Yong, H., Wong, Y.H., Yijun, C. & Xin Sun (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literatüre. Decision Support Systems, 50, 559–569.
• Osuna, R.G. (2002). Lecture Notes CS 790: Introduction to pattern recognition. Ohio: Wright State University. Özkan, Y. (2013). Veri Madenciliği Yöntemleri. İstanbul: Papatya Yayıncılık Eğitim.
• Pandey, U.K. & Pal S. (2011). Data Mining: A prediction of performer or underperformer using classification. International Journal of Computer Science and Information Technologies, 2, 2, 686-690.
• Perols, J. (2011). Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms. Auditing: A Journal of Practice and Theory, 30, 2, 19-50.
• Ravisankar, P., Ravi, V., Rao, G.R. & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support System, 50, 491-500.
• Rejesus R.M., Little, B.B. & Lovell, A.C. (2004). Using data mining to detect crop insurance fraud: Is there a role for social scientists? Journal of Financial Crime, 12, 1, 24–32.
• Rodrıguez, J. D., Perez A. & Lozano J. A. (2010). Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation. Transactions On Pattern Analysis And Machine Intelligence, 32, 3, 569-575.
• Sermaye Piyasasında Bağımsız Denetim Standartları Hakkında Tebliğ- Seri X, No:22, Kısım 18, md.2
• Sharma, A. & Panigrahi, P. K. (2012). A Review of Financial Accounting Fraud Detection based on Data Mining Techniques. International Journal of Computer Applications, 39–1, 37-47.
• Shmueli, G., Nitin, R. P., Peter, C. B. (2010). Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner. Hoboken: John Wiley and Sons.
• Singh, Y.P. (2007). Accounting and Financial Management For IT Professionals. New Delhi: New Age International Limited Publishers,.
• Soria, D., Garibaldi, J. M., Ambrogi, F., Biganzoli, E. M. & Ellis I. O. (2011). A ‘non-parametric’ version of the naive Bayes classifier. Knowledge-Based Systems, 24, 775–784. Swartz, N. (2004). IBM, Mayo clinic to mine medical data. The Information Management Journal, 38, 6.
• Terzi, S. (2012). Hile Ve Usulsüzlüklerin Tespitinde Veri Madenciliğinin Kullanımı. Muhasebe ve Finansman Dergisi, 54, 51-64.
• Togun, N. K. & Sedat Baysec (2010). Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks. Applied Energy, 87, 349–355.
• Wan, C. H., Lee, L. H., Rajkumar, R. & Dino, I. (2012). A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine. Expert Systems with Applications, 39, 15, 11880-11888.
• Weng, S.-S., Chiu, R.-K., Wang, B.-J. & Su S.-H. (2006/2007). The study and verification of mathematical modeling for customer purchasing behavior. Journal of Computer Information Systems, 47, 2, 46–57.
• Yücel, E. (2013). Hileli Finansal Raporlamanın Tespitinde Kırmızı Bayrakların Etkinliği: Türkiye Uygulaması. Muhasebe ve Finansman Dergisi, 60, 139-158