27-12-2011, 07:04 PM
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08-02-2012, 05:58 PM
[/size][/font]I need the ppt for this topic analysis on credit card fraud detection method..
09-02-2012, 11:21 AM
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18-03-2012, 03:39 AM
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18-03-2012, 03:44 AM
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19-03-2012, 12:40 PM
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26-06-2013, 04:55 PM
Analysis on Credit Card Fraud Detection Methods
Analysis on Credit.pdf (Size: 148.97 KB / Downloads: 42) 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 various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria. INTRODUCTION The Credit Card Is A Small Plastic Card Issued To Users As A System Of Payment. It Allows Its Cardholder To Buy Goods And Services Based On The Cardholder's Promise To Pay For These Goods And Services. Credit Card Security Relies On The Physical Security Of The Plastic Card As Well As The Privacy Of The Credit Card Number. Globalization And Increased Use Of The Internet For Online Shopping Has Resulted In A Considerable Proliferation Of Credit Card Transactions Throughout The World. Thus A Rapid Growth In The Number Of Credit Card Transactions Has Led To A Substantial Rise In Fraudulent Activities. Credit Card Fraud Is A Wide-Ranging Term For Theft And Fraud Committed Using A Credit Card As A Fraudulent Source Of Funds In A Given Transaction. Credit Card Fraudsters Employ A Large Number Of Techniques To Commit Fraud. To Combat The Credit Card Fraud Effectively, It Is Important To First Understand The Mechanisms Of Identifying A Credit Card Fraud. Over The Years Credit Card Fraud Has Stabilized Much Due To Various Credit Card Fraud Detection And Prevention Mechanisms. RELATED WORKS Fraud detection involves monitoring the behavior of users in order to estimate, detect, or avoid undesirable behavior. To 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]. COMPARISON OF VARIOUS FRAUD DETECTION SYSTEMS PARAMETERS USED FOR COMPARISON The Parameters used for comparison of various Fraud Detection Systems are Accuracy, Fraud Detection Rate in terms of True Positive and false positive, cost and training required, Supervised Learning. The comparison performed is shown in Table 1. Accuracy: It represents the fraction of total number of transactions (both genuine and fraudulent) that have been detected correctly. Method: It describes the methodology used to counter the credit card fraud. The efficient methods like sequence alignment, machine learning, neural networks are used to detect and counter frauds in credit card transactions. True Positive (TP): It represents the fraction of fraudulent transactions correctly identified as fraudulent and genuine transactions correctly identified as genuine. False Positive (FP): It represents fraction of genuine transactions identified as fraudulent and fraudulent transactions identified as genuine.Training data: It consists of a set of training examples. The fraud detection systems are initially trained with the normal behavior of a cardholder. CONCLUSION Efficient credit card fraud detection system is an utmost requirement for any card issuing bank. Credit card fraud detection has drawn quite a lot of interest from the research community and a number of techniques have been proposed to counter credit fraud. The Fuzzy Darwinian fraud detection systems improve the system accuracy. Since The Fraud detection rate of Fuzzy Darwinian fraud detection systems in terms of true positive is 100% and shows good results in detecting fraudulent transactions. The neural network based CARDWATCH shows good accuracy in fraud detection and Processing Speed is also high, but it is limited to one-network per customer. The Fraud detection rate of Hidden Markov model is very low compare to other methods. |
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