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Investigation of Data Mining Techniques in Fraud Detection: Credit Card

by R. Gayathri, A. Malathi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 82 - Number 9
Year of Publication: 2013
Authors: R. Gayathri, A. Malathi
10.5120/14143-1560

R. Gayathri, A. Malathi . Investigation of Data Mining Techniques in Fraud Detection: Credit Card. International Journal of Computer Applications. 82, 9 ( November 2013), 12-15. DOI=10.5120/14143-1560

@article{ 10.5120/14143-1560,
author = { R. Gayathri, A. Malathi },
title = { Investigation of Data Mining Techniques in Fraud Detection: Credit Card },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 9 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 12-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number9/14143-1560/ },
doi = { 10.5120/14143-1560 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:57:48.837586+05:30
%A R. Gayathri
%A A. Malathi
%T Investigation of Data Mining Techniques in Fraud Detection: Credit Card
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 9
%P 12-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent times the more secure data transfer takes place almost by means of internet. Apart from the corporate companies, publics also started using the network media. At the same time the risk also increases in secure data transfer. One of the major issue among them is credit card fraud detection systems which has a significant percentage of transactions labeled as fraudulent are in fact legitimate. Thus this may delay the fraudulent transaction detection. Due to ever increasing volumes of data needed to be analyzed using data mining methods and techniques which are being used more and more. The aim of this study is to analyze the five most frequently used classification techniques in fraudulent detection. Neural Network, Decision Tree, Naïve Bayes, K-nn and Support Vector Machine are taken in to consideration. This paper discusses on each techniques and their limitations. Still they suffer from the problem of false detection rate highly.

References
  1. Elkan, C. (2001). Magical Thinking in Data Mining: Lessons from CoIL Challenge 2000. Proc. of SIGKDD01, 426-431.
  2. Lavrac, N. , Motoda, H. , Fawcett, T. , Holte, R. , Langley, P. & Adriaans, P. (2004). Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. Machine Learning 57(1-2): 13-34.
  3. Brause, R. , Langsdorf, T. and Hepp, M. (1999). Neural data mining for credit card fraud detection Proceedings 11th IEEE International Conference on Tools with Artificial Intelligence. tao guo, gui-yang li, neural data mining for credit card fraud detection 978-1-4244-2096-4/08 ©2008 ieee, 3630, july 2008.
  4. Mirjana Pejic-Bach, Profiling intelligent systems applications in fraud detection and prevention: survey of research articles, 2010 International Conference on Intelligent Systems, Modeling and Simulation.
  5. Prabin Kumar Panigrahi, A Framework for Discovering Internal Financial Fraud using Analytics, International Conference on Communication Systems and Network Technologies 2011.
  6. Sahin, Y. , Duman, E. : An overview of business domains where fraud can take place, and a survey of various fraud detection techniques. In: Proceedings of the 1st International Symposium on Computing in Science and Engineering, Aydin, Turkey (2010).
  7. V. Filippov L. Mukhanov B. Shchukin Credit Card Fraud Detection System.
  8. Y. Sahin, E. Duman "Detecting Credit Card Fraud by ANN and Logistic Regression" 2011.
  9. http://www. forbes. com/sites/halahtouryalai/2012/10 /22/countries-with-the-most-card-fraud-u-s-and-mexico/.
  10. Raghavendra Patidar, Lokesh Sharma, Credit Card Fraud Detection Using Neural Network, NCAI2011, 13-14 May 2011, Jaipur, India, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-NCAI2011, June 2011.
  11. Kokkinaki, A. (1997). On Atypical Database Transactions: Identification of Probable Frauds using Machine Learning for User Profiling. Proc. of IEEE Knowledge and Data Engineering Exchange Workshop, 107-113.
  12. Fan, W. , Miller, M. , Stolfo, S. , Lee, W. & P Chan. 2001. Using Artificial Anomalies to Detect Unknown and Known Network Intrusions, Proc. of ICDM01; 123-248.
  13. Dr R. Dhanapal , Gayathiri. P, Credit Card Fraud Detection Using Decision Tree For Tracing Email And Ip, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 2, September 2012.
  14. John, George H, and Pat Langley. "Estimating Continuous Distributions in Bayesian Classifiers. ".
  15. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. SanMateo: Morgan Kaufmann Publishers, 1995. 338-345.
  16. Aha, David W. , Dennis Kibler, and Marc K. Albert. "Instance-based learning algorithms. " Machine Learning, 1991: 37-66.
  17. Cortes, Corinna, and Vladimir Vapnik. "Support-vector networks. " Machine Learning, 1995: 273-297.
  18. Witten, Ian, and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. San Fransico: Elsevier, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Neural Networks Decision Tree Support Vector Machines Credit Card Fraud Detection

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