CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Improving Credit Card Fraud Detection using a Meta-Classification Strategy

by Joseph Pun, Yuri Lawryshyn
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 10
Year of Publication: 2012
Authors: Joseph Pun, Yuri Lawryshyn
10.5120/8930-3007

Joseph Pun, Yuri Lawryshyn . Improving Credit Card Fraud Detection using a Meta-Classification Strategy. International Journal of Computer Applications. 56, 10 ( October 2012), 41-46. DOI=10.5120/8930-3007

@article{ 10.5120/8930-3007,
author = { Joseph Pun, Yuri Lawryshyn },
title = { Improving Credit Card Fraud Detection using a Meta-Classification Strategy },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 10 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number10/8930-3007/ },
doi = { 10.5120/8930-3007 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:58:30.950318+05:30
%A Joseph Pun
%A Yuri Lawryshyn
%T Improving Credit Card Fraud Detection using a Meta-Classification Strategy
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 10
%P 41-46
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These "false alarms" delay the detection of fraudulent transactions and can cause unnecessary concerns for customers. In this study, over 1 million unique credit card transactions from 11 months of data from a large Canadian bank were analyzed. A meta-classifier model was applied to the transactions after being analyzed by the Bank's existing neural network based fraud detection algorithm. This meta-classifier model consists of 3 base classifiers constructed using the decision tree, naïve Bayesian, and k-nearest neighbour algorithms. The naïve Bayesian algorithm was also used as the meta-level algorithm to combine the base classifier predictions to produce the final classifier. Results from the research show that when a meta-classifier was deployed in series with the Bank's existing fraud detection algorithm improvements of up to 28% to their existing system can be achieved.

References
  1. P. K. Chan and S. J. Stolfo, "Experiments in Multistrategy Learning by Meta-Learning," Proceedings of the second international conference on Information and knowledge management, pp. 314-323, 1993.
  2. A. L. Prodromidis and S. J. Stolfo, "A Comparative Evaluation of Meta-Learning Strategies over Large and Distributed Data Sets," in Proceedings of the ICML-99 Workshop on Recent Advances in Meta-learning and Future Work, Ljubljana, 1999.
  3. S. Bhattacharyya, S. Jha, K. Tharakunnel and C. Westland, "Data mining for credit card fraud: A comparative study," Decision Support Systems, pp. 602-613, 2011.
  4. S. Ghosh and D. L. Reilly, "Credit card fraud detection with a neural network," in Proceedings of the 27th Hawaii International Conference on System Sciences, Los Alamitos, CA, 1994.
  5. S. Maes, K. Tuyls, B. Vanschoenwinkel and B. Manderick, "Credit Card Fraud Detection Using Bayesian and Neural Networks," in Proceedings of the 1st International NAISO Congress on Neuro Fuzzy Technologies, Havana, Cuba, 2002.
  6. C. Chiu and C. Tsai, "A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection," in Proceedings of 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004.
  7. D. Patil, S. Karad, V. Wadhai, J. Gokhale and P. Halgaonkar, "Efficient Scalable Multi-Level Classification Scheme for Credit Card Fraud Detection," Internation Journal of Computer Science and Network Security, vol. 10, no. 8, pp. 123-130, 2010.
  8. Q. Lu and C. Ju, "Research on Credit Card Fraud Detection Model Based on Class Weighted Support Vector Machine," Journal of Convergence Information Technology, p. 62, 2011.
  9. A. Srivastava, A. Kundu, S. Sural and A. K. Majumdar, "Credit Card Fraud Detection Using Hidden Markov Model," IEEE Transactions on Dependable and Secure Computing, vol. 5, no. 1, pp. 37-48, 2008.
  10. P. L. Chan and S. J. Stolfo, "Toward Scalable Learning with Non-uniform Class and Cost Distributions: A Case Study in Creidt Card Fraud Detection," Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pp. 164-168, 1998.
  11. R. Brause, T. Langsdorf and M. Hepp, "Neural Data Mining for Credit Card Fraud Detection," in Proceedings of the 11th IEEE International Conference on Tools with Arti?cial Intelligence, Silver Spring, 1999.
  12. C. Phua, D. Alahakoon and V. Lee, "Minority Report in Fraud Detection: Classification of Skewed Data," SIGKDD Explorations, pp. 50-59, 2004.
  13. E. Duman and H. Ozcelik, "Detecting Credit Card Fraud by Genetic Algorithm and Scatter Search," Expert Systems with Applications, pp. 13057-13063, 2011.
  14. P. K. Chan, An Extensible Meta-Learning Approach for Scalable and Accurate Inductive Learning, 1996.
  15. A. Bradley, "The Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms," Pattern Recognition, vol. 30, no. 7, pp. 1145-1159, 1997.
  16. R. Vilalta and Y. Drissi, "A Perspective View and Survey of Meta-Learning," Artificial Intelligence Review, vol. 18, no. 2, pp. 77-95, 2002.
  17. J. R. Quinlan, C4. 5: Programs for machine learning, San Francisco: Morgan Kaufmann, 1993.
  18. G. H. John and P. Langley, "Estimating Continuous Distributions in Bayesian Classifiers," in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, SanMateo, 1995.
  19. D. W. Aha, D. Kibler and M. K. Albert, "Instance-based learning algorithms," Machine Learning, pp. 37-66, 1991.
Index Terms

Computer Science
Information Sciences

Keywords

Meta-classification

Powered by PhDFocusTM