Analysis on Credit Card Fraud Detection Methods

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Analysis on Credit Card Fraud Detection Methods
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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 intelligence, Meta learning and pattern matching. This paper compares and analyzes some of the good techniques that have been used in detecting credit card fraud. It focuses on credit card fraud detection methods like Fusion of Dempster Shafer and Bayesian learning [2][5][12][15][25], Hidden Markov Model [3], Artificial neural networks and Bayesian Learning approach [5][25],BLAST and SSAHA Hybridization[1][6][11][14][24], Fuzzy Darwinian System[4]. Section II gives an overview about those techniques. Section III presents a comparative survey of those techniques and section IV summarizes the fraud detection techniques. A. A fusion approach using Dempster–Shafer theory and Bayesian learning FDS of Dempster–Shafer theory and Bayesian learning Dempster–Shafer theory and Bayesian learning is a hybrid approach for credit card fraud detection [2][5][12][15] which combines evidences from current as well as past behavior. Every cardholder has a certain type of shopping behavior,

Abstract— Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of...
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