Date of Publication :18th June 2022
Abstract: With the advancement of technology, the use of credit cards for shopping is also increasing. Since credit cards are the most popular payment method, errors on these credit cards are also increasing. This white paper presents the techniques used to detect credit card fraud as an overview paper. Fraud detection should be performed as soon as it is executed. Fraud detection methods are constantly evolving to prevent or prevent criminals from taking over their strategies. The transaction was determined to be normal, abnormal, or suspicious based on the initial assumptions. When a transaction is determined to be suspicious, their beliefs are further strengthened or weakened by comparing their similarities to fraudulent or real transactions using Bayesian learning
Reference :
-
- 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, January March 2008.
- Suman and Nutan “Review paper on credit card fraud detection”, International Journal of Computer Trends and Technology (IJCTT) – volume 4 Issue 7– July 2013.
- L. Delamaire, H. Abdou and J. Poinon, “Credit card fraud and detection techniques: a review”, Banks and Bank Systems, Volume 4, Issue 2, 2009.
- Phua, D. Alahakoon and V. Lee, “Minority report in fraud detection: classification of skewed data,” ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 50-59, 2004.
- S. Esakkiraj and S. Chidambaram, “A predictive approach for fraud detection using hidden Markovv model” International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 1, January- 2013 C.
- Y. Sahin and E. Duman, “Detecting credit card fraud by decision trees and support vector machines”, International Multiconference of Engineers and computer scientists March, 2011
- R.J. Bolton and D.J. Hand “Unsupervised profiling methods for fraud detection”, Department of Mathematics Imperial College London {r.bolton, d.j.hand}@ic.ac.uk
- Z. Ferdousi and A. Maeda “Unsupervised outlier detection in time series data”, Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW'06) © 2006 IEEE.
- J.S. Mishra, S. Panda, and A. Kumar Mishra, “A novel approach for credit card fraud detection targeting the Indian market” IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 3, No 2, May 2013 ISSN (Print): 1694- 0814 | ISSN (Online): 1694-0784 www.IJCSI.org
- K. Rama Kalyani and D. Uma Devi, “Fraud detection of credit card payment system by genetic algorithm”, International Journal of Scientific & Engineering Research Volume 3, Issue 7, July- 2012.
- Ray-I Chang, Liang-Bin Lai, Wen-De Su, Jen-Chieh Wang and Jen Shiang Kouh “Intrusion detection by back propagation neural networks with samplequery and attribute-query”, Research India Publications; (2006). (6-10).
- R. Patidar and L. Sharma ,“Credit card fraud detection using neural network” NCAI2011, 13- 14 May 2011, Jaipur, India International Journal of Soft Computing and Engineering (IJSCE) ISSN: 22312307, Volume-1, Issue-NCAI2011, June 2011.
- B. Subashini and Dr. K. Chitra “Enhanced system for revealing fraudulence in credit card approval”, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 8, August – 2013 ISSN: 2278-0181.
- L. Phua, V. lee, K. Smith and R. Gayler, “A comprehensive survey of data mining-based fraud detection research”, School of Business Systems, Faculty of Information Technology, Monash University, Clayton campus, Wellington Road, Clayton, Victoria 3800, Australia.
- M. A. bagheri, Q. GAO and S. Escalera “Logo recognition based on the dempster-shafer fusion of multiple classifiers”, Advances in Artificial Intelligence, Lecture Notes in Computer Science Volume 7884, 2013, pp 1-12.
- Maes, S., Tuyls, K., Vanschoenwinkel, B. & Manderick, B. (2002), “Credit card fraud detection using bayesian and neural networks”, Proc. of the 1st International.