An Assessment on Credit Card Fraud Detection: Survey
Keywords:
Credit Card Fraud Detection, Classification, Ensemble Techniques, Random Forest, Decision Trees
Abstract
Credit card fraud is a costly problem for many financial institutions, costing businesses billions of dollars a year. Many adversaries still escape fraud detection systems because these systems often do not include information about the adversary's knowledge of the fraud detection mechanism. This thesis aims to include information on the motivations of "crooks" and the knowledge base in an adaptive fraud detection system. In this thesis, we use a theoretical adversarial learning approach to classification to model the best fraudster strategy. We proactively adapt the fraud detection system to classify these future fraudulent transactions better. Therefore, this document aims to provide an over-supervised bird's-eye approach with a suitable feature extraction technique that improves fraud detection rather than mistakenly classifying an actual transaction as fraud.
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References
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[2]. Alejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic, Björn Ottersten, “Feature engineering strategies for credit card fraud detection”, Expert Systems with Applications, Volume 51, 2016, Pages 134-142,
[3]. Y. Saygin, A. Reisman and YunTong Wang, "Value of information gained from data mining in the context of information sharing," in IEEE Transactions on Engineering Management, vol. 51, no. 4, pp. 441-450, Nov. 2004.
[4]. C. Jiang, J. Song, G. Liu, L. Zheng and W. Luan, "Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism," in IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1-1.
[5]. K. Randhawa, C. K. Loo, M. Seera, C. P. Lim and A. K. Nandi, "Credit card fraud detection using AdaBoost and majority voting," in IEEE Access, vol. PP, no. 99, pp. 1-1
[6]. S. M. S. Askari and M. A. Hussain, "Credit card fraud detection using fuzzy ID3," 2017 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, 2017, pp. 446-452.
[7]. Charleonnan, "Credit card fraud detection using RUS and MRN algorithms," 2016 Management and Innovation Technology International Conference (MITicon), Bang-San, 2016, pp.
[8]. DUDA, R. O. AND HART, P. E., 1973. "Pattern Classification and Scene Analysis”. John Wiley and Sons, Inc., New York, NY.
[9]. MAO, J. AND JAIN, A. K., 1996. "A self-organizing network for hyperellipsoidal clustering (HEC)”. IEEE Trans. Neural Netw. 7, 16–29.
[10]. Angiulli, F. (2009) “Outlier Detection Techniques for Data Mining” John Wang (Ed.), Encyclopedia of Data Warehousing and Mining, Second Edition, Pp. 1483-1488.
[11]. Dubes, R. (1993). “Cluster analysis and related issue”. In Handbook of Pattern Recognition and Computer Vision, C. Chen, L. Pau, and P. Wang, Eds., River Edge, NJ: World Science Publishing Company, pp. 3 – 32.
[12]. Gordon, A. (1998). “Cluster validation”. In Data Science, Classification, and Related Methods, C. Hayashi, N. Ohsumi, K. Yajima, Y. Tanaka, H. Bock, and Y. Bada, Eds., New York, NY: Springer - Verlag, pp. 22 – 39.
[13]. Halkidi, M., Batistakis, Y., and Vazirgiannis, M. (2002). "Cluster validity methods": Part I & II, SIGMOD Record, 31 (2 & 3).
[14]. Dubes, R. (1993). “Cluster analysis and related issue". In Handbook of Pattern Recognition and Computer Vision, C. Chen, L. Pau, and P. Wang,
Eds., River Edge, NJ: World Science Publishing Company, pp. 3 – 32.
[15]. Rand, W. (1971). “Objective criteria for the evaluation of clustering methods”. Journal of the American Statistical Association, 66: 846 – 850.
[16]. Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis". Computational and Applied Mathematics 20: 53–65.
[17]. L. Hubert, J. Schultz. “Quadratic assignment as a general data-analysis strategy”.1976. British Journal of Mathematical and Statistical Psychologie. 29. 190-241.
[18]. P. Jaccard. “The distribution of flora in the alpine zone”. 1912. New Phytologist. 11, 37–50.
[19]. Fujikawa, Y. and Ho, T. (2002). “Cluster-based algorithms for dealing with missing values”.In Cheng, M.-S., Yu, P. S., and Liu, B., editors, Advances in Knowledge Discovery and Data Mining, Proceedings of the 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, volume 2336 of Lecture Notes in Computer Science, pages 549–554. New York: Springer.
[20]. W.M. Rand. “Objective criteria for the evaluation of clustering methods”. 1971. Journal of the American Statistical Association. 846-850.
[21]. Everitt, B., Landau, S., and Leese, M. (2001). "Cluster analysis”, 4th edition. London: Arnold
[22]. Hartigan, J. (1975). “Clustering algorithms”. New York, NY: John Wiley & Sons
[23]. ROSENFELD, A. AND KAK, A. C., 1982. "Digital Picture Processing”. 2nd ed. Academic Press, Inc., New York, NY.
Published
2020-10-25
How to Cite
Malviya, A., & Yadav, H. (2020). An Assessment on Credit Card Fraud Detection: Survey. International Journal of Advanced Computer Technology, 9(5), 12-15. Retrieved from https://ijact.org/index.php/ijact/article/view/61
Section
Articles
