Analysis on Credit Card Fraud Detection Methods
Analysis on Credit Card Fraud Detection Methods
1 1
S. Benson Edwin Raj, 2A. Annie Portia Assistant Professor (SG), P.G., 2Scholar Department of CSE Karunya University, Coimbatore counter the credit card fraud effectively, it is necessary to understand the technologies involved in detecting credit card frauds and to identify various types of credit card frauds [20] [21] [22] . There are multiple algorithms for credit card fraud detection [21] [29]. They are artificial neural-network models which are based upon artificial intelligence and machine learning approach [5] [7] [9] [10] [16], distributed data mining systems [17] [19], sequence alignment algorithm which is based upon the spending profile of the cardholder [1] [6], intelligent decision engines which is based on artificial intelligence [23], Meta learning Agents and Fuzzy based systems [4]. The other technologies involved in credit card fraud detection are Web Services-Based Collaborative Scheme for Credit Card Fraud Detection in which participant banks can share the knowledge about fraud patterns in a heterogeneous and distributed environment to enhance their fraud detection capability and reduce financial loss [8] [13], Credit Card Fraud Detection with Artificial Immune System [13] [26], CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection [18] which is bases upon data mining approach [17] and neural network models, the Bayesian Belief Networks [25] which is based upon artificial intelligence and reasoning under uncertainty will counter frauds in credit cards and also used in intrusion detection [26], case-based reasoning for credit card fraud detection [29], Adaptive Fraud Detection which is based on Data Mining and Knowledge Discovery [27], Real-time credit card fraud using computational intelligence [28], and Credit card fraud detection using self-organizing maps [30]. Most of the credit card fraud detection systems mentioned above are based on artificial
1 1
S. Benson Edwin Raj, 2A. Annie Portia Assistant Professor (SG), P.G., 2Scholar Department of CSE Karunya University, Coimbatore counter the credit card fraud effectively, it is necessary to understand the technologies involved in detecting credit card frauds and to identify various types of credit card frauds [20] [21] [22] . There are multiple algorithms for credit card fraud detection [21] [29]. They are artificial neural-network models which are based upon artificial intelligence and machine learning approach [5] [7] [9] [10] [16], distributed data mining systems [17] [19], sequence alignment algorithm which is based upon the spending profile of the cardholder [1] [6], intelligent decision engines which is based on artificial intelligence [23], Meta learning Agents and Fuzzy based systems [4]. The other technologies involved in credit card fraud detection are Web Services-Based Collaborative Scheme for Credit Card Fraud Detection in which participant banks can share the knowledge about fraud patterns in a heterogeneous and distributed environment to enhance their fraud detection capability and reduce financial loss [8] [13], Credit Card Fraud Detection with Artificial Immune System [13] [26], CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection [18] which is bases upon data mining approach [17] and neural network models, the Bayesian Belief Networks [25] which is based upon artificial intelligence and reasoning under uncertainty will counter frauds in credit cards and also used in intrusion detection [26], case-based reasoning for credit card fraud detection [29], Adaptive Fraud Detection which is based on Data Mining and Knowledge Discovery [27], Real-time credit card fraud using computational intelligence [28], and Credit card fraud detection using self-organizing maps [30]. Most of the credit card fraud detection systems mentioned above are based on artificial
References: 155 International Conference on Computer, Communication and Electrical Technology – ICCCET2011, 18th & 19th March, 2011