A Review Of Credit Card Fraud Detection Techniques In Electronic Finance And Banking

References

[1] P. K. Chan, W. Fan, A. L. Prodromidis, and S. J. Stolfo, “Distributed Data Mining in Credit Card Fraud Detection,” IEEE Intelligent Systems, pp. 67-74, 1999.

[2] P. Jayant, Vaishali, and D. Sharma, “Survey on Credit Card Fraud detection Techniques,” International Journal of Engineering Research & Technology, Vol. 3, No. 3, pp. 1545-1551, 2014.

[3] V. Patil, and U. K. Lilhore, “A Survey on Different Mining and Machine Learning Methods for Credit Card Fraud Detection,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 3, No. 5, pp. 320-325, 2018.

[4] T. P. Bhatla, V. Prabhu, and A. Dua, “Understanding Credit Card Frauds,” Cards Business Review, pp. 1-15, 2003.

[5] S.K.Saravanan1, and G.N.K. Suresh Babu, “An Improving Credit Card Fraud Detection Using Novel Data Mining Techniques,” International Journal of Current Engineering and Scientific Research, Vol. 4, No. 10, pp. 17-23, 2017.

[6] M. Zareapoor, and P. Shamolmoali, “Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier,” Procedia Computer Science 48, pp. 679-685, 2015.

[7] S. Patil, V. Nemade, and P. Soni, “Predictive Modelling for Credit Card Fraud Detection Using Data Analytics,” Procedia Computer Science 132, pp. 385-395, 2018

[8] S. Agarwal, and S. Upadhay, “A Fraud Detection Approach Using Clustering Based Method,” Journal of Basic and Applied Engineering Research, Vol. 1, No. 10, pp. 33- 37, 2014.

[9] B. Pushpalatha, and C. W. Joseph, “Credit Card Fraud Detection Based on the Transaction by Using Data Mining Techniques,” Vol. 5, No. 2, pp. 1785-1793, 2017

[10] Sodasoltaniziba, and M. Alibalafr, “The Study of Fraud Detection in Financial and Credit Institutions with Real Data,” Global Journal of Computer Science and Technology (C) Software and Data Engineering, Vol. 15, No. 6, pp. 37-45, 2015.

[11] M. R. Lepoivre, C. O. Avanzini, Guillaune Bignon, L. Legendre, and A. K. Piwele, “Credit Card Fraud Detection with Unsupervised Algorithms,” Journal of Advances in Information, Vol. 7, No. 1, pp. 34-38, 2016.

[12] A. Pouramirarsalani, M. Khalilian, and A. Nikravanshalmani, “Fraud Detection in E- banking by using Hybrid Feature election and Evolutionary algorithms,” International Journal of Computer and Network Security, Vol. 17, No. 8, pp. 271-279, 2017.

[13] M. Carminati, R. Caron, F. Maggi, I. Epifani, and S. Zanero, “BANKSEALER: An Online Banking Fraud Analysis and Decision Support System,” N. Cuppens-Boulahia et al. (Eds.): SEC 2014, IFIP AICT 428, pp. 380–394, 2014.

[14] K. Randhawa, C. K. Loo, M. Seera, C. P. Lim, and A. K. Nandi, “Credit Card Fraud Detection using Adaboost and Majority Voting,” IEEEAccess, Vol. XX, pp. 1-8, 2017.

[15] A. Rajaei, “Identification of Fraud in Banking Data and Financial Institutions Using Classification Algorithms,” International Journal of Information, Security and Sytems and Management, Vol. 6, No.2, pp. 653-667, 2017

[16] R. Rajamani, and M. Rathika, “Credit Card Fraud Detection Using Hidden Markov Model and Neural Network,” Proceedings of the UGC Sponsored National Conference on Advanced Networking and Applications, Special Issue Published in Int. Journal. Of Advanced Networking and Applications, pp. 175-179, 2015

[17] R. Sarno, R. D. Dewandono, T. Ahmad, M. F. Naufal, and F. Sinaga, “Hybrid Association Rule Learning and Process Minning for Fraud detection,” IAENG International Journal of Computer Science, Vol. 42, No. 2, 2015

[18] S. S. Baboo, and N. Preetha, “Analysis of Spending Pattern on Credit Card Fraud Detection,” IOSR Journal of Computer Engineering (IOSR-JCE) Vol. 17, Issue 2, pp. 61-64, 2015

[19] K. R. Seeja, and M. Zareapoor, “FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining,” Hindawi Publishing Corporation, The Scientific World Journal, pp. 1-10, 2014.

[20] N. Malini, and M. Pushpa, “Analysis on Credit Card Fraud Detection Techniques by Data Mining and Big Data Approach,” International Journal of Research in Computer Applications and Robotics, Vol. 5, No. 5, pp. 38-45, 2017

[21] Y. Dai, J. Yan, X. Tang, H. Zhao, and M. Guo, “Online Credit Card Fraud Detection: A Hybrid Frame Work with Big Data Technologies,” 2016 IEEE TrustCom - BigDataSE-ISPA, pp. 1644-1651.

[22] R. Banerjee, G. Bourla, S. Chen, M. Kashyap, S. Purohit, and J. Battipaglia, “Comparative Analysis of Machine Learning Algorithm through Credit Card Fraud Detection,” New Jersey Governor‟s School of Engineering and Technology, pp. 1-10, 2018

[23] N. Khare, and S. Y. Sait, “Credit Card Fraud Detection Using Machine Learning Models and Collating Machine Learning Model,” International Journal of Pure and Applied Mathematics, Vol. 118, No. 28, pp. 825-838, 2018.

[24] M. Carminati, M. Polino, A. Continella, A. Lanzi, F. Maggi, and S. Zanero, “Security Evaluation of a Banking Fraud Analysis System,” ACM Transactions on Privacy and Security, Vol. 1, No. 1, pp. 1-30, 2018

[25] S. Sharma, P. Mitta, and Geetika,”An Approach to Detect Credit Card Frauds Using Attribute Selection and Ensemble Techniques,” international Journal Journal of Computer Applications, Vol. 180, No. 21, pp. 1-6, 2018.

[26] S. Kumari and A. Choubey, “A Review on Various Techniques and Approaches for Credit Card Treachery Detection,” International Journal of Scientific Research Engineering and Technology, Vol. 6 No. 5, pp. 485-489, 2017

@article{1701528,
      author = {Chilaka, U. L., G. A. Chukwudebe, Bashiru, A.},
      title = {A Review Of Credit Card Fraud Detection Techniques In Electronic Finance And Banking},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {2},
      pages = {456-467},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1701528.pdf},
      abstract = {The growing innovation in technology has been exploited in the finance/banking sector in the form of electronic commerce (e-commerce). Financial transaction is the backbone of global market. Rather than make transaction conventionally, cashless transaction is being done in recent times using credit cards. The credit card transaction uses electronic technology to make commerce easy, and its use for such purposes has increased. However, credit card being the most common means of payment in the society today, has witnessed increased number of fraud cases. In order to reduce or solve the problems of fraud cases related to credit card transactions, several methods have been proposed and implemented in literature and real-world. This paper discussed some of the recent fraud detection techniques that have been developed to identify and detect fraudulent cases of credit card transactions. Since fraud detection consist of detecting fraud  as soon as possible once it is done, the various models developed are built to provide fast response, accuracy, high degree of sensitivity, effectiveness and efficiency. These techniques have their individual benefits and limitations. However, it is recommended that a hybrid model with robust and optimal performance that combines various be used for fraud detection in finance/banking. The paper has also presented observed challenges of credit card techniques.},
      keywords = {Credit card transaction, cashless transaction, e-commerce, Fraud detection techniques},
      month = {August},
  }