The week's pick
Random Articles
Reseach Article
A Novel Idea for Credit Card Fraud Detection using Decision Tree
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 161 - Number 13 |
| Year of Publication: 2017 |
| Authors: Prajal Save, Pranali Tiwarekar, Ketan N. Jain, Neha Mahyavanshi |
10.5120/ijca2017913413
|
Prajal Save, Pranali Tiwarekar, Ketan N. Jain, Neha Mahyavanshi . A Novel Idea for Credit Card Fraud Detection using Decision Tree. International Journal of Computer Applications. 161, 13 ( Mar 2017), 6-9. DOI=10.5120/ijca2017913413
Abstract
Online shopping and banking has increased by the growth of internet and by use of credit card. Along with this number of credit card fraud is also increased. Many modern techniques based on Artificial Intelligence, Data warehousing has evolved in detecting various credit card fraudulent transactions. We proposed a system which detect fraud in credit card transaction processing using a decision tree with combination of Luhn's algorithm and Hunt's algorithm. Luhn’s algorithm is used to validate the card number. Address matching rule checks whether the Billing Address and Shipping Address match or not. This check does not guarantee whether a transaction is fraud or genuine. But if the two addresses match, the transaction can be classified as genuine with a high probability. Else, the transaction is labelled as suspect. A customer usually carries out similar types of transactions in terms of amount, which can be visualized as part of a cluster. Since a fraudster is likely to differ from the customer’s account, his transactions can be detected as exceptions to the cluster – a process known as outlier detection.
References
- M. Syeda, Y.Q. Zhang, Y. Pan, “Parallel granular neural networks for fast credit card fraud detection”, Proceedings of the IEEE International Conference on Fuzzy Systems, 2002, pp. 572–577.
- S. Maes, K. Tuyls, B. Vanschoenwinkel, B. Manderick, “Credit card fraud detection using Bayesian and neural networks”, Proceedings of the First International NAISO Congress on Neuro Fuzzy Technologies, 2002
- R.C. Chen, M.L. Chiu, Y.L. Huang, L.T. Chen, “Detecting credit card fraud by using questionnaire-responded transaction model based on support vector machines”, Proceedings of the Fifth International Conference on Intelligent Data Engineering and Automated Learning, vol. 3177, October 2004, pp. 800–806.
- R.C. Chen, S.T. Luo, X. Liang, V.C.S. Lee, “Personalized approach based on SVM and ANN for detecting credit card fraud”, Proceedings of the IEEE International Conference on Neural Networks and Brain, October 2005, pp.810–815.
- P.K. Chan, W. Fan, A.L. Prodromidis, S.J. Stolfo, “Distributed data mining in credit card fraud detection”, Proceedings of the IEEE Intelligent Systems, 1999, pp.67–74.
- R. Brause, T. Langsdorf, M. Hepp, “Neural data mining for credit card fraud detection”, Proceedings of the International Conference on Tools with Artificial Intelligence, 1999, pp. 103–106.
- C. Chiu, C. Tsai, “A web services-based collaborative scheme for credit card fraud detection”, Proceedings of the IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004, pp. 177–181.
- E. Aleskerov, B. Freisleben, and B. Rao, “CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection”, Computational Intelligence for Financial Eng., pp. 220-226, 1997.
- M.J. Kim and T.S. Kim, “A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection,” Proc. Int‟l Conf.Intelligent Data Eng. and Automated Learning, pp. 378-383, 2002.
- K.RamaKalyani, D.UmaDevi, “Fraud Detection of Credit Card Payment System by Genetic Algorithm”, International Journal of Scientific & Engineering Research Volume 3, Issue 7, July-2012.
- Bidgoli, B. M., Kashy, D., Kortemeyer, G. & Punch, W. F “Predicting student performance: An Application of data mining methods with the educational web-based. (2003). System LON-CAPA”. In Proceedings of ASEE/IEEE frontiers in education conference. . (2003).
- Ekrem Duman, M. Hamdi Ozcelik “Detecting credit card fraud by genetic algorithm and scatter search”. Elsevier, Expert Systems with Applications, (2011). 38; (13057–13063).
- Abhinav Srivastava, Amlan Kundu, Shamik Sural and Arun K. Majumdar, "CreditCard Fraud Detection Using Hidden Markov Model" IEEE, Transactions On Dependable And Secure Computing, Vol. 5, No 1. , January-March 2008
- V. Bhusari, and S. Patil, “Study of Hidden Markov Model in Credit Card Fraudulent Detection”, International Journal of Computer Applications (0975 – 8887) Volume 20– No.5, April 2011
- V.Bhusari ,S.Patil ," Study of Hidden Markov Model in Credit Card Fraudulent Detection ",International Journal of Computer Applications (0975 - 8887) Volume 20- No.5, April 2011
- S. Benson Edwin Raj, A. Annie Portia “Analysis on Credit Card Fraud Detection Methods”. IEEE-International Conference on Computer, Communication and Electrical Technology; (2011). (152-156).
- Ray-I Chang, Liang-Bin Lai, Wen-De Su, Jen-Chieh Wang, Jen-Shiang Kouh “Intrusion Detection by Backpropagation Neural Networks with Sample-Query and Attribute-Query”. Research India Publications; (2006). (6-10).
- Raghavendra Patidar, Lokesh Sharma “Credit Card Fraud Detection Using Neural Network”. International Journal of Soft Computing and Engineering (IJSCE), (2011). Volume-1, Issue; (32-38).
Index Terms
Keywords