ABSTRACT
Credit card payment is called CNP which stands for "card not present". Security issues about CNP are critical. We propose an empirical study research for analyzing payment transactions using K-means clustering and decision table method of data mining techniques. The data sets we used are credit card payment from government open data website and credit card fraud data set shared from Weka. In this paper, we found that EC (Electronic Commerce) payment was the majority credit card payment fraud from the first data set and got a close look about similar attributes of fraud customer payments.
- Bolton, Richard J.; Hand, David J. Statistical fraud detection: A review. Statistical science, 2002, 235--249.Google Scholar
- Changsu Kim a,1, Wang Tao a,2, Namchul Shin b,*, Ki-Soo Kim. An empirical study of customers' perceptions of security and trust in e-payment systems, Electronic Commerce Research and Applications 9(2010) 84--95.Google ScholarCross Ref
- Ekerm Duman, M. Hamdi Ozcelik. Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Application 38(2011)13057--13063Google Scholar
- Fahmi, Marwan, Abeer Hamdy, and Khaled Nagati. Data Mining Techniques for Credit Card Fraud Detection: Empirical Study. Sustainable Vital Technologies in Engineering and Informatics BUE ACE1. Elsevier Ltd, 2016. 1--9.Google Scholar
- G. Parthasarathya, L. Ramanathanb, Y. JustinDhasc, J. Saravanakumard and J. Darwine. Comparative Case Study of Machine Learning Classification Techniques Using Imbalanced Credit Card Fraud Datasets, International Conference on Sustainable Computing in Science, Technology & Management (SUSCOM-2019)Google Scholar
- Hall, Mark A., and Eibe Frank. Combining naive bayes and decision tables. FLAIRS conference. Vol. 2118. 2008.Google Scholar
- Huysmans, Johan, et al. An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems 51.1 (2011): 141--154.Google ScholarDigital Library
- https://data.gov.tw/dataset/11703/Google Scholar
- https://en.wikipedia.org/wiki/Credit_card_fraudGoogle Scholar
- https://en.wikipedia.org/wiki/Weka_(machine_learning)Google Scholar
- Hegarty, J. et al. A trust model for consumer internet shopping. International Journal of Electronic Commerce, 6, 1, 2003, 75--91.Google Scholar
- Kohavi, Ron. The power of decision tables. European conference on machine learning. Springer, Berlin, Heidelberg, 1995.Google ScholarDigital Library
- MacQueen, James. Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Vol. 1. No. 14. 1967.Google Scholar
- Mukherjee, S.; Mukherjee, T.; Nath, A. Fraud Analytics Using Data Mining. International Journal of Research Studies in Computer Science and Engineering (IJRSCSE), 2016, 3.4: 1--11.Google Scholar
- Narendra Sharma, Aman Bajpai, Mr. Ratnesh Litoriya. Comparison the various clustering algorithms of Weka tools. International Journal of Emerging Technology and Advanced Engineering (ISSN 2250-2459), 2012, 2--5Google Scholar
- PANIGRAHI, Suvasini, et al. Credit card fraud detection: A fusion approach using Dempster--Shafer theory and Bayesian learning. Information Fusion, 2009, 10.4: 354--363.Google Scholar
- Quah, J.T.S., & Srinagesh, M. Real-time credit fraud detection using computational intelligence. Expert Systems with Applications, 35, 1721--1732Google ScholarDigital Library
- Raghavendra Patidar, Lokesh Sharma. Credit card Fraud Detection Using neural network. International Journal of Soft Computing and Engineering (IJSCE)ISSN: 2231-2307, Volume-1, Issue-NCAI2011, June 2011Google Scholar
- Safavian, S. Rasoul, and David Landgrebe. "A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics 21.3 (1991): 660--674.Google Scholar
- Yee, Ong Shu; SAGADEVAN, Saravanan; MALIM, Nurul Hashimah Ahamed Hassain. Credit card fraud detection using machine learning as data mining technique. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 2018, 10.1-4: 23--27.Google Scholar
Index Terms
- Fraud Payment Research: Payment through Credit Car
Recommendations
Comprehensive study on methods of fraud prevention in credit card e-payment system
iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & ServicesDue to the increasing demand on electronic payment, fraud methods also increasing which resulted in losing millions of Riyals worldwide each year. Several ways have been applied to fight against fraud, therefore, fraud prevention is becoming a vital ...
Data mining for credit card fraud: A comparative study
Credit card fraud is a serious and growing problem. While predictive models for credit card fraud detection are in active use in practice, reported studies on the use of data mining approaches for credit card fraud detection are relatively few, possibly ...
Mobile Payment Fraud: A Practical View on the Technical Architecture and Starting Points for Forensic Analysis of New Attack Scenarios
IMF '15: Proceedings of the 2015 Ninth International Conference on IT Security Incident Management & IT Forensics (imf 2015)As payment cards and mobile devices are equipped with Near Field Communication (NFC) technology, electronic paymenttransactions at physical Point of Sale (POS) environments are changing. Payment transactions do not require the customerto insert their ...
Comments