Real Time Data-Driven Approaches for Credit Card Fraud Detection-Reference-Cited by-同舟云学术

Real Time Data-Driven Approaches for Credit Card Fraud Detection

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 International Conference on E-Business and Applications - ICEBA 2018
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Author:

Tran Phuong Hanh¹,Tran Kim Phuc²,Huong Truong Thu³,Heuchenne Cédric⁴,HienTran Phuong⁵,Le Thi Minh Huong⁶

Affiliation:

1. Department of Economics, Danang, Architecture University, Division of Artificial Intelligence, Dong A University Research Institute, Dong A University, Danang, Vietnam

2. Division of Artificial Intelligence, Dong A University Research Institute, Dong A University, Danang, Vietnam, LMBA, Université de Bretagne-Sud, Vannes, France

3. Department of Communication Engineering, School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam

4. HEC Management School, University of Liège, Liège, Belgium

5. Division of Artificial Intelligence, Dong A University, Danang, Vietnam

6. Department of Economics, Danang Architecture University, Danang, Vietnam

Publisher

ACM Press

Reference30 articles.

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2. Bhatla, T. P., Prabhu, V., and Dua, A. 2003. Understanding credit card frauds. Cards Bus. Rev., 1(6).

3. Holmes, T. E. 2015. Credit card fraud and ID theft statistics.

4. Consumer Sentinel Network Data Book for January - December 2016. Federal Trade Commission, 2017.

5. Ilonen, J., Paalanen, P., Kamarainen, J. K., and Kalviainen, H. 2006. Gaussian mixture pdf in one-class classification: computing and utilizing confidence values. in Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. 2, 577--580.

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