Supervised Machine (SVM) Learning for Credit Card Fraud Detection

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-8 Number-3                          
Year of Publication : 2014
Authors : Sitaram patel , Sunita Gond


Sitaram patel , Sunita Gond."Supervised Machine (SVM) Learning for Credit Card Fraud Detection ", International Journal of Engineering Trends and Technology(IJETT), 8(3),137-139 February 2014. ISSN:2231-5381. published by seventh sense research group


The growth of e commerce increases the money transaction via electronic network which is designed for hassle free fast & easy money transaction. The facility involves greater risk of misuse for fraud one of them is credit card fraud which can happen by many types as by stolen card, by INTERNET hackers who can hack your system & get important information about your card or by information leakage during the transaction. Several researchers have proposed their work for credit card fraud detection by characterizing the user spending profile. In this thesis we are proposing the SVM (Support Vector Machine) based method with multiple kernel involvement which also includes several fields of user profile instead of only spending profile. The simulation result shows improvement in TP (true positive), TN (true negative) rate, & also decreases the FP (false positive) & FN (false negative) rate.


[1] Abhinav Srivastava, Amlan Kundu, Shamik Sural, Arun Majumdar,( Jan.-Mar. 2008) “Credit Card Fraud Detection Using Hidden Markov Model," IEEE Transactions on Dependable and Secure Computing, vol. 5, no. 1, pp. 37-48,.
[2] Wen-Fang Yu, Na Wang,( 2009.)“Research on Credit Card Fraud Detection Model Based on Distance Sum International Joint Conference on Artificial Intelligence ," JCAI, pp.353-356,
[3] Sushmito Ghosh and Douglas L. Reilly Nestor,( 1994.) “Credit Card Fraud Detection with a Neural-Network,” Inc. Proceedings of the Twenty-Seventh Annual Hawaii International Conference on System Sciences,pp.621-630 ,
[4] Aihua Shen, Rencheng Tong (2007), Yaochen Deng School of Management, Graduate University of the Chinese Academy of Sciences, Beijing, 100084, China, “Application of Classification Models on Credit Card Fraud Detection”,pp.1-4,IEEE.
[5] Chun-Hua JU, Na Wang,( 2009 ) “ Credit Card Fraud Detection Model Based on Similar Coefficient Sum,” First International Workshop on Database Technology and Applications ,pp.295 - 298 , Database Technology and Applications, First International Workshop on Wuhan, Hubei .
[6] V. N. Vapnik,( 1995.)” The Nature of Statistical Learning Theory,” New York: Springer-Verlag,Second Edition, Technical report,
[7] V. Kecman,( 2001)“Learning and Soft Computing: Support Vector Machines, Neutral Networks and Fuzzy Logic Models,”Cambridge, MA: MIT Press,Technical report,
[8] Research Article Bird Species Recognition Using Support Vector Machines Hindawi(2007) Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume, Article ID 8637,8pages doi : 10.1155 / 2007 / 38637.
[9] “Global Consumer Attitude Towards On-Line Shopping,”(Mar. 2007). shopping.pdf,
[10] D.J. Hand, G. Blunt, M.G. Kelly, and N.M. Adams,( 2000.) “Data Miningfor Fun and Profit,” Statistical Science, vol. 15, no. 2, pp. 111-131,
[11] “Statistics for General and On-Line Card Fraud,( Mar. 2007) “http://www.,.
[12] S. Ghosh and D.L. Reilly(1994.), “Credit Card Fraud Detection with a Neural-Network,” Proc. 27th Hawaii Int’l Conf. System Sciences:Information Systems: Decision Support and Knowledge-Based Systems,vol. 3, pp. 621-630,
[13] M. Syeda, Y.Q. Zhang, and Y. Pan,( 2002). “Parallel Granular Networks for Fast Credit Card Fraud Detection,” Proc. IEEE Int’l Conf. Fuzzy Systems, pp. 572-577,
[14] S.J. Stolfo, D.W. Fan, W. Lee, A.L. Prodromidis, and P.K. Chan(1997),“Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results,” Proc. AAAI Workshop AI Methods in Fraud and Risk Management, pp. 83-90,.
[15] S.J. Stolfo, D.W. Fan, W. Lee, A. Prodromidis, and P.K. Chan, (2000) “Cost-Based Modeling for Fraud and Intrusion Detection: Results from the JAM Project,” Proc. DARPA Information Survivability Conf. and Exposition, vol. 2, pp. 130-144,.

MATLAB, e-commerce, online banking, classification support vector machine