An Intelligent System for Fraud Detection in Coin Futures Market’s Transactions of Iran Mercantile Exchange Based on Bayesian Network

Document Type : Research Paper

Authors

1 Assistant Prof., Dep. of IT., Faculty of Management, Kharazmi University, Tehran, Iran

2 MSc. Student in Decision Sciences and Knowledge Engineering, Faculty of Management Kharazmi University, Tehran, Iran

Abstract

In order to gain more illicit profit, some traders in the stock market try to make a targeted impact on prices by placing fake orders and false advertising. Due to the high customer population, it is not possible to discover these frauds using traditional methods. The present study seeks to provide a system for preventing the frauds in future market-trading coins based on Bayesian classifier model for Iran Mercantile Exchange. The proposed model has polynomial time complexity and high accuracy because of considering important dependencies among different features of data. The primary labeling of data has been done by Kmeans clustering. The test of model shows 94.55 percent similarity between model's output and labeled data. Using this system can helps to identify the fraudulent from non-fraudulent traders.

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Main Subjects

References

تقوا، م. ر.؛ منصوری، ط.؛ فیضی، ک.؛ اخگر، ب. (1395). کشف تقلب در تراکنش‏های کارت‏های بانکی با استفاده از پردازش موازی ناهنجاری در بزرگ‌داده. نشریۀ مدیریت فناوری اطلاعات، 8(3)، 477-498.
تقوی­فرد، م.ت.؛ جعفری، ز. (1394). کشف تقلب در بیمۀ بدنۀ خودرو با بهره‎مندی از سیستم خبرۀ فازی. نشریۀ مدیریت فناوری اطلاعات، 7(2)، 258-239.
صفری، ح.؛ حشمتی­پور، ف.؛ مهرابی، ع.؛ نصابی، و. (1391). مدل­سازی عوامل مؤثر بر به اشتراک‎گذاری اطلاعات در زنجیرۀ تأمین شرکت ایران‎خودرو خراسان با استفاده از روش ترکیبی نگاشت علّی و شبکه­های بیزین. نشریۀ مدیریت فناوری اطلاعات، 4(10)، 92-65.
فلاح شمس، م.؛ کردلوئی، ح.ر. (1390). آزمون مدل‎های لاجیت و شبکۀ عصبی مصنوعی جهت پیش‎بینی دستکاری قیمت در بورس اوراق بهادار تهران. مجلۀمهندسیمالیومدیریتاوراقبهادار، 2(7)، 69-37.
فلاح شمس، م.؛ کردلوئی، ح. ر.؛ رشنو، م. (1391). بررسی دستکاری قیمت­ها در بورس اوراق بهادار تهران با استفاده از مدل ماشین بردار پشتیبان. مجلۀ تحقیقات مالی، (14)1، 84- .69
مدیریت مطالعات اقتصادی. (1391). دستکاری بازار، ترفندها و ابزارهای مطالعه. شرکت بورس کالای ایران.
وثوق، م.؛ تقوی­فرد، م. ت.؛ البرزی، م. (1393). شناسایی تقلب در کارت‌های بانکی با استفاده از شبکه‌های عصبی مصنوعی. نشریۀ مدیریت فناوری اطلاعات، 6(4)، 746-721.
Barber, D. (2010). Bayesian Reasoning and Machine Learning; first edition.  Cambridge: University Press.
Chen, Y., Miao, D. & Zhang, H. (2010). Neighborhood outlier detection. Expert Systems with Applications, 37(12), 8745-8749.
Cheng, J., Bell, D. & Weiru, L. (1998). Learning Bayesian networks from data: An efficient approach based on information theory. On World Wide Web at http://www. Cs. ualberta. ca/~ jcheng/bnpc. Htm.
Fallah Shams, M. & Kordlouei, H. (2011). Logit Model Test and Neural Network to Predict Price Manipulation in Tehran Stock Exchange. Financial Engineering and Portfolio Management, 2(7), 37-69. (in Persian)
Fallah Shams, M., Kordlouei, H. & Rashnow, M. (2013). Investigating the Price Manipulation in Tehran Stock Exchange by Using the SVM Model. Financial Research, University of Tehran, 1(14), 69-84. (in Persian)
Ferdousi, Z. & Maeda, A. (2006). Unsupervised Fraud Detection in Time Series data. In Proceedings. 22nd International Conference on Data Engineering Workshops, Atlanta, Georgia, 3-7 April 2006.
Franke, M., Hoser, B. & Schröder, J. (2008). On the analysis of irregular stock market trading behavior. In Data Analysis, Machine Learning and Applications (pp. 355-362). Springer Berlin Heidelberg.
Friedman, N., Iftach, N. & Dana, P. (1999). Learning Bayesian network structure from massive datasets: the sparse candidate algorithm. Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc.
Golmohammadi, K., Zaiane, O. R. & Díaz, D. (2014, October). Detecting stock market manipulation using supervised learning algorithms. In Data Science and Advanced Analytics (DSAA). 2014 International Conference on (pp. 435-441). IEEE.
Heckerman, D, Geiger, D., &Chickering, D M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine learning20.3: 197-243.
Kim, Y. & Sohn, S.Y. (2012). Stock fraud detection using peer group analysis. Expert Systems with Applications, 39(10), 8986-8992.
Korb, K. B. & Nicholson, A.E. (2010). Bayesian artificial intelligence. London: CRC press.
Lee, E.J., Eom, K.S. & Park, K.S. (2013). Microstructure-based manipulation: Strategic behavior and performance of spoofing traders. Journal of Financial Markets, 16(2), 227-252.
Liu, H. & Setiono, R. (1995, November). Chi2: Feature selection and discretization of numeric attributes. In Tools with artificial intelligence, 1995. proceedings., seventh international conference on (pp. 388-391). IEEE.
The management of Economic Studies. (2012). Market Manipulation, Tricks & Prevention Tools. Iran Mercantile Exchange.
Mitchell, T. M. (1997). Machine learning. Burr Ridge, IL: McGraw Hill.
Nielsen, T. D. & Jensen, F. V. (2009). Bayesian networks and decision graphs. Springer Science & Business Media.
Olszewski, D. (2014). Fraud detection using self-organizing map visualizing the user profiles. Knowledge-Based Systems, 70, 324-334.
Pearl, J. (1982). Reverend Bayes on inference engines: A distributed hierarchical approach. AAAI'82 Proceedings of the Second AAAI Conference on Artificial Intelligence. Pennsylvania, August 18 – 20.
Safari, H., Heshmatipour, F., Mehrabi, A. & Nesabi, V.R. (2012). Modeling of Factors Affecting Information Sharing in Supply Chain of IKKCO Using the Integrated Cognitive Mapping Method and Bayesian Networks. The Journal of Information Technology Management, 4(10), 65-92 (in Persian)
Taghavifard, S. M. & Jafari, Z. (2015). Fraud Detection Using a Fuzzy Expert System in Motor Insurance. The Journal of Information Technology Management, 7(2), 239- 258. (in Persian)
Taghva, M. R., Mansouri, T., Feizi, K., Akhgar, B. (2016). Fraud Detection in Credit Card Transactions; Using Parallel Processing of Anomalies in Big Data. The Journal of Information Technology Management, 8(3), 477-498. (in Persian)
Thoppan, J.J. & Punniyamoorthy, M. (2013). Market manipulation and surveillance–a survey of literature and some practical implications. International Journal of Value Chain Management, 7(1), 55-75.
Vosough, M., Taghavifard, M. & Alborzi, M. (2015). Bank Card Fraud Detection Using Artificial Neural Network. The Journal of Information Technology Management, 6(4), 721-746. (in Persian)
Yao, Y., Zhai, J., Cao, Y. & Ding, X. (2015). WITHDRAWN: Static and dynamic models: A framework for price manipulation detection. Expert Systems with Applications. Available in: http://www.elsevier.com/locate/withdrawalpolicy
Journal of Information Technology Management
Volume 9, Issue 1
2017
Pages 1-20

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