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Credit Card Fraud Detection System In Nigeria Banks Using Adaptive Data Mining And Intelligent Agents: A Review
[Full Text]
AUTHOR(S)
Amanze, B.C., Onukwugha, C.G
KEYWORDS
Fraud Transaction, Data Mining, Intelligent Agents, Credit Card, Spending Pattern.
ABSTRACT
The growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Conventional method of identification based on possession of pin and password are not all together reliable. Higher acceptability and convenience of credit card for purchases has not only given personal comfort to customers but also attracted a large number of attackers. As a result, credit card payment systems must be supported by efficient fraud detection capability for minimizing unwanted activities by fraudster’s. Most of the well-known algorithms for fraud detection are based on supervised training. Every cardholder has a certain shopping behaviour, which establishes an activity profile for him. Existing fraud detection systems try to capture behavioural patterns as rules which are static. This becomes ineffective when cardholder develops new patterns. This paper aimed at developing credit card fraud detection system for banking industries in Nigeria using adaptive data mining and intelligent agents that could combines evidences from current as well as past behaviour and to determine the suspicious level of each incoming transaction. The statistics of fraud in Nigeria is discussed. In this paper, a system model for credit card fraud is discussed and designed.
REFERENCES
[1]. Jia, W.U. & Jongwoo, P. (2005). Intelligent Agents and Fraud Detection.
[2]. Hand, D. J. (2002). Fraud Detection in Telecommunications and Banking. Technimetrics, 52(1) 34-38.
[3]. Nabha, K., Neha, P., Shraddha, K., Suja, S., & Amol, P. (2015). Credit card fraud detection system using Hidden Markov Model and Adaptive Communal Detection. International Journal of Computer Science and Information Technologies, 6 (2), 1795-1797.
[4]. Singh Mandeep, Perminderpal Singh & Rajan Kumar (2014). Fraud detection by monitoring user behavior and activities. International Conference on Computer and Intelligent Systems, 6-14.
[5]. Sahil Hak, Suraj Singh, & Varun Purohit. (2015). Credit card fraud detection using Advanced Combination Heuristic and Bayes’ Theorem. International Journal of Innovative Research in Computer and Communication Engineering. 3(4), 2756-2763
[6]. Ekrem, D., & Mehmet Hamdi Ozcelik (2011). Detecting Credit Card fraud by genetic algorithms and scatter search. Expert Systems with applications: An International Journal, 38(10), 13057-13063.
[7]. NeFF Pledges Sustained Fight Against e-fraud in 2016.
[8]. Cho, B., & Park, H. (2003). Efficient Anomaly Detection by Modeling Privilege Flows Using Hidden Markov Model. Computer and Security, 22(I), 45-55.
[9]. Delamaire, L., Hussein, A., & John P (2009). Credit card fraud and detection techniques: a review, Banks and Bank Systems, 4(2), 57-68.
[10]. Joshi, S., & Phoha, V. (2005). Investigating Hidden Markov Models Capabilities in Anomaly Detection. Proc. 43rd ACM Ann. Southeast Regional Conf, 1, (), 98-103.
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