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Credit Card Transaction Fraud Detection by using Hidden Markov Model
Nitin Mishra,Ranjit Kumar,Shishir Kumar Shandilya
International Journal of Scientific Engineering and Technology , 2012,
Abstract: this paper proposes a HMM (Hidden Markov Model) based fraud detection system for credit card fraud detection. The method works on the statistical behavior of user’s transactions. Since the original transactions are not available due to privacy policies of bank we used heresynthetically generated data for a credit card user, and then HMM model is trained using different size of sample of generated labeled data we also discuss the performance of the HMM model on this data set in terms of detection accuracy and earliness of fraud detection. The system has been tested on a Pentium 4 PC with 2 GB of RAM, the test program is coded in MATLAB 7.5.
Application of Hidden Markov Model in Credit Card Fraud Detection  [PDF]
V. Bhusari,S. Patil
International Journal of Distributed and Parallel Systems , 2011,
Abstract: In modern retail market environment, electronic commerce has rapidly gained a lot of attention and alsoprovides instantaneous transactions. In electronic commerce, credit card has become the most importantmeans of payment due to fast development in information technology around the world. As the usage ofcredit card increases in the last decade, rate of fraudulent practices is also increasing every year.Existing fraud detection system may not be so much capable to reduce fraud transaction rate.Improvement in fraud detection practices has become essential to maintain existence of payment system.In this paper, we show how Hidden Markov Model (HMM) is used to detect credit card fraud transactionwith low false alarm. An HMM based system is initially studied spending profile of the card holder andfollowed by checking an incoming transaction against spending behavior of the card holder, if it is notaccepted by our proposed HMM with sufficient probability, then it would be a fraudulent transaction.
A parameter optimized approach for improving credit card fraud detection  [PDF]
Prakash,Chandrasekar
International Journal of Computer Science Issues , 2013,
Abstract: The usage of credit cards has highly increased due to high-speed innovation in the electronic commerce technology. Since credit card turns out to be the majority well-liked manner of payment for mutually online as well as habitual purchase, cases of fraud correlated through it are as well increasing. In normal Hidden Markov Model the problem of cannot find an optimal state sequence for the underlying Markov process also this observed sequence cannot be viewed as training a model to best fit the observed data. In this research, the main aim is to model the sequence of observations in credit card transaction processing using an Advanced Hidden Markov Model (AHMM) and show how it can be utilized for the exposure of frauds. In this process an AHMM is initially trained with the regular manners of a cardholder. If an incoming credit card transaction is not recognized by the trained AHMM with adequately high probability, it is believed to be fraudulent. This proposed work desire to regulate the model parameters to best fit the observations. The ranges of the matrices (N and M) are fixed but the elements of A,B and #960; are to be decided, focus to the rank stochastic condition. The information that can efficiently re-estimate the model itself is one of the more incredible features of HMMs this referred here as AHMM.
Credit Card Fraud Detection Analysis
J. Keziya Rani #1 , S. Prem Kumar*2, U. Ram Mohan#3 , C. Uma Shankar*4
International Journal of Computer Trends and Technology , 2011,
Abstract: Computer security and certain aspects of cyber crime is beeing increasing day by day. The detailed study of the present day most commonly encountered cyber crime like Credit card fraud analysis is presented in this paper . The model reported in this paper is based on Hidden Markov Model(HMM), is a markov chain for which the state is only partially observable. In HMM model , We quantize the purchase values x into M price ranges V1; V2; . . . VM, forming the observation symbols at the issuing bank. The actual price range for each symbol is configurable based on the spending habit of individual cardholders. These price ranges can be determined dynamically by applying a clustering algorithm on the values of each cardholder’s transactions, In this work, we consider only three price ranges, namely, low (l), medium (m), and high(h). Our set of observation symbols is, therefore, V fl; m; hg making M 3
METHOD AND SYSTEM FOR DETECTING FRAUD IN CREDIT CARD TRANSACTION
VIVEK KUMAR PRASAD
International Journal of Innovative Research in Computer and Communication Engineering , 2013,
Abstract: Due to a rapid advancement in the electronic commerce technology. 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 this paper I am introducing the concept of three level of security, the first level is the static User name or password, and in the second level it uses Hidden Markov Model (HMM) and shows how it can be used for the detection of frauds. An HMM is initially trained with the normal behaviour of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. And to reduce the false positive transactions we will send the dynamic password, which can be send through the use of web services to the user’s mobile phone number instantly and he/she has to enter same password for getting the authorization from the bank side and suppose if due to the heavy load on the server side , if the user does not get the password in its mobile phone within the given stipulated time , then after a little time interval some personnel questions(either security question or images) will be asked which can be answered by the end user
Credit Card Fraud Detection & Prevention of Fraud Using Genetic Algorithm  [PDF]
Rinky D.Patel,Dheeraj Kumar Singh
International Journal of Soft Computing & Engineering , 2013,
Abstract: Companies and institutions move parts of their business, or the entire business, towards online services providing e-commerce, information and communication services for the purpose of allowing their customers better efficiency and accessibility. Payment card fraud has become a serious problem throughout the world. Companies and institutions loose huge amounts annually due to fraud and fraudsters continuously seek new ways to commit illegal actions. In this we will try to detect fraudulent transaction through the with the genetic algorithm. Genetic algorithm are used for making the decision about the network topology, number of hidden layers, number of nodes that will be used in the design of neural network for our problem of credit card fraud detection.
Credit Card Fraud: The study of its impact and detection techniques  [PDF]
Khyati Chaudhary,Bhawna Mallick
International Journal of Computer Science and Network , 2012,
Abstract: With the rise and swift growth of E-Commerce, credit card usesfor online purchases has increased dramatically and it causedsudden outbreak in the credit card fraud. Fraud is one of themajor ethical issues in the credit card industry. With both onlineas well as regular purchase, credit card becomes the most popularmode of payment with cases of fraud associated with it are alsoincreasing. A clear framework on all these approaches willcertainly lead to an efficient credit card fraud detection system.Currently, for simplicity reasons, all the base learners for creditcard fraud detection use the same desired distribution. It would beinteresting to implement and evaluate the credit card frauddetection system by using very large databases with skewed classdistributions and non-uniform cost per error. This paper presentsa analysis of cost incurred in credit card fraud detection on dataset.
FRAUD DETECTION IN CREDIT CARD SYSTEM USING WEB MINING  [PDF]
HETVI MODI,SHIVANGI LAKHANI,NIMESH PATEL,VAISHALI PATEL
International Journal of Innovative Research in Computer and Communication Engineering , 2013,
Abstract: Now a day the usage of credit cards has dramatically increased. 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. Various techniques like classification, clustering and apriori of web mining will be integrated to represent the sequence of operations in credit card transaction processing and show how it can be used for the detection of frauds. Initially, web mining techniques trained with the normal behaviour of a cardholder. If an incoming credit card transaction is not accepted by the web mining model with sufficiently high probability, it is considered to be fraudulent. At the same time, the system will try to ensure that genuine transactions will not be rejected. Using data from a credit card issuer, a web mining model based fraud detection system will be trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity. Web mining techniques can be trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud, and mail-order fraud. The proposed system will be able to detect frauds by considering a cardholder’s spending habit without its significance. Usually, the details of items purchased in individual transactions are not known to any Fraud Detection System. The proposed system will be an ideal choice for addressing this problem of current fraud detection system. Another important advantage of proposed system will be a drastic reduction in the number of False Positives transactions. FDS module of proposed system will receive the card details and the value of purchase to verify, whether the transaction is genuine or not. If the Fraud Detection System module will confirm the transaction to be of fraud, it will raise an alarm, and the transaction will be declined.
Detecting Credit Card Fraud by Using Support Vector Machines and Neural Networks
Rong-Chang Chen,Luo Shu-Ting,Li Shiue-Shiun
International Journal of Soft Computing , 2012,
Abstract: Conventionally, historical actual transaction data are used to set up a model for detecting credit card fraud. Instead of using traditional approaches, a new personalized approach has recently been presented to prevent fraud. The personalized approach proposes to prevent credit card fraud before initial use of a new card, even users without any real transaction data. Though this approach is promising, there are some problems waiting to be improved. A main issue of the personalized approach is how to predict accurately with only few training data, since it collects quasi-real transaction data by using an online questionnaire system and users are generally not willing to spend too much time to answer questionnaires. This study employs Support Vector Machines (SVM) and Artificial Neural Networks (ANN) to investigate the problem of fraud detection of credit cards. The type of ANN models we use in this study is the Back Propagation Networks (BPN). The performance of neural networks is compared with that from SVM. Experimental results from this study show that both BPN and SVM can offer good solutions. When the data number is small, SVM can have better prediction performance than BPN in predicting the future data. Besides, the average prediction accuracy reaches a maximum when the training data ratio arrives at 0.8.
Credit Card Fraud Detection using Decision Tree for Tracing Email and IP  [PDF]
R.Dhanapal,Gayathiri.P
International Journal of Computer Science Issues , 2012,
Abstract: Credit card fraud is a wide-ranging term for theft and fraud committed using a credit card or any similar payment mechanism as a fraudulent source of funds in a transaction. The purpose may be to obtain goods without paying, or to obtain unauthorized funds from an account. Transactions completed with credit cards seem to become more and more popular with the introduction of online shopping and banking. Correspondingly, the number of credit card frauds has also increased .Currently; data mining is a popular way to combat frauds because of its effectiveness. Data mining is a well-defined procedure that takes data as input and produces output in the forms of models or patterns. In other words, the task of data mining is to analyze a massive amount of data and to extract some usable information that we can interpret for future uses. Frauds has also increased .Currently, data mining is a popular way to combat frauds because of its effectiveness. Data mining is a well-defined procedure that takes data as input and produces output in the forms of models or patterns. In other words, the task of data mining is to analyze a massive amount of data and to extract some usable information that we can interpret for future uses.
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