CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Study of Hidden Markov Model in Credit Card Fraudulent Detection

by V. Bhusari, S. Patil
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 20 - Number 5
Year of Publication: 2011
Authors: V. Bhusari, S. Patil
10.5120/2428-3263

V. Bhusari, S. Patil . Study of Hidden Markov Model in Credit Card Fraudulent Detection. International Journal of Computer Applications. 20, 5 ( April 2011), 33-36. DOI=10.5120/2428-3263

@article{ 10.5120/2428-3263,
author = { V. Bhusari, S. Patil },
title = { Study of Hidden Markov Model in Credit Card Fraudulent Detection },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 20 },
number = { 5 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 33-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume20/number5/2428-3263/ },
doi = { 10.5120/2428-3263 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:07:00.632532+05:30
%A V. Bhusari
%A S. Patil
%T Study of Hidden Markov Model in Credit Card Fraudulent Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 20
%N 5
%P 33-36
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The most accepted payment mode is credit card for both online and offline in today’s world, it provides cashless shopping at every shop in all countries. It will be the most convenient way to do online shopping, paying bills etc. Hence, risks of fraud transaction using credit card has also been increasing. In the existing credit card fraud detection business processing system, fraudulent transaction will be detected after transaction is done. It is difficult to find out fraudulent and regarding loses will be barred by issuing authorities. Hidden Markov Model is the statistical tools for engineer and scientists to solve various problems. In this paper, it is shown that credit card fraud can be detected using Hidden Markov Model during transactions. Hidden Markov Model helps to obtain a high fraud coverage combined with a low false alarm rate.

References
  1. Federal Trade Commission, 2009. Consumer sentinel network data book.
  2. Statistics for General and On-Line Card Fraud, March 2007.
  3. Global Consumer Attitude towards On-Line Shopping, March 2007.
  4. Ghosh, S., and Reilly, D.L., 1994. Credit Card Fraud Detection with a Neural-Network, 27th Hawaii International l Conference on Information Systems, vol. 3 (2003), pp. 621-630.
  5. Syeda, M., Zhang, Y. Q., and Pan, Y., 2002 Parallel Granular Networks for Fast Credit Card Fraud Detection, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 572-577 (2002).
  6. Stolfo, S. J., Fan, D. W., Lee, W., Prodromidis, A., and Chan, P. K., 2000. Cost-Based Modeling for Fraud and Intrusion Detection: Results from the JAM Project, Proceedings of DARPA Information Survivability Conference and Exposition, vol. 2 (2000), pp. 130-144.
  7. Aleskerov, E., Freisleben, B., and Rao, B., 1997. CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection, Proceedings of IEEE/IAFE: Computational Intelligence for Financial Eng. (1997), pp. 220-226.
  8. Fan, W., Prodromidis, A. L., and Stolfo, S. J., 1999. Distributed Data Mining in Credit Card Fraud Detection, IEEE Intelligent Systems, vol. 14, no. 6 (1999), pp. 67-74.
  9. Brause, R., Langsdorf, T., and Hepp, M., 1999. Neural Data Mining for Credit Card Fraud Detection, Proceedings of IEEE International Conference Tools with Artificial Intelligence (1999), pp. 103-106.
  10. Chiu, C., and Tsai, C., 2004. A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection, Proceedings of IEEE International Conference e-Technology, e-Commerce and e-Service (2004), pp. 177-181.
  11. Phua, C., Lee, V., Smith, K., and Gayler, R., 2007. A Comprehensive Survey of Data Mining-Based Fraud Detection Research (2007), March.
  12. Rabiner, L.R. 1989. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of IEEE, vol. 77, no. 2 (1989), pp.257-286.
  13. Ourston, D., Matzner, S., Stump, W., and Hopkins, B., 2003. Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks, Proceedings of 36th Annual Hawaii International Conference System Sciences, vol. 9 (2003), pp. 334-344.
  14. Cho, S.B., and Park, H.J., 2003. Efficient Anomaly Detection by Modeling Privilege Flows Using Hidden Markov Model, Computer and Security, vol. 22, no. 1 (2003), pp. 45-55.
  15. Kim, M.J., and Kim, T.S., 2002. A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection, Proceedings of International Conference on Intelligent Data Eng. and Automated Learning, (2002), pp. 378-383.
  16. Kaufman, L., and Rousseeuw, P.J., 1990. Finding Groups in Data: An Introduction to Cluster Analysis, Wiley Series in Probability and Math. Statistics, (1990).
Index Terms

Computer Science
Information Sciences

Keywords

Hidden Markov Model fraud transaction credit card

Powered by PhDFocusTM