15-03-2011, 02:36 PM
Presented by:
B.SURESH
E.SOMAIAH
[attachment=10204]
CREDIT CARD FRAUD DETECTION USING HIDDEN MARKOV MODEL
ABSTRACT
Due to a rapid advancement in the electronic commerce technology, the use 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.
In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds
An HMM is initially trained with the normal behavior 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.
We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature
EXISTING SYSTEM
In case of the existing system the fraud is detected after the fraud is done that is, the fraud is detected after the complaint of the holder. And so the card holder faced a lot of trouble before the investigation finish.
And also as all the transaction is maintained in a log, we need to maintain a huge data, and also now a day’s lot of online purchase are made so we don’t know the person how is using the card online, we just capture the ip address for verification purpose
There need a help from the cyber crime to investigate the fraud.
To avoid the entire above disadvantage we propose the system to detect the fraud in a best easy way
PROPOSED SYSTEM
In this system ,we present a hidden markov model(HMM) Which does not required fraud signatures and yet is able to detect frauds by considering a cardholder’s spending habit.
Card transaction processing sequence by the stochastic process of an HMM. The details of items purchased in individual transactions are usually not known to an Fraud Detection System (FDS) running at the bank that issues credit cards to the cardholder.
Hence, we feel that HMM is an ideal choice for addressing this problem.
An FDS runs at a credit card issuing bank. Each incoming transaction is submitted to the FDS for verification. FDS receives the card details and the values if purchases to verify, whether the transaction is genuine or not.
The types of goods that are bought in that transaction are not known to the FDS.
It tries to find anomaly in the transaction based on the spending profile of the cardholder, shipping address and billing addresses.
If the FDS confirms the transaction to be
malicious, it raises an alarm, and the issuing bank declines the transaction.
The concerned cardholder may then be contacted and alerted about the possibility that the card is compromised.
ADVANTAGES
The detection of the fraud use of the card is found much faster that existing system.
In case of the existing system even the original card holder is also checked for fraud detection. But in this system no need check the original user as we maintain a log.
B.SURESH
E.SOMAIAH
[attachment=10204]
CREDIT CARD FRAUD DETECTION USING HIDDEN MARKOV MODEL
ABSTRACT
Due to a rapid advancement in the electronic commerce technology, the use 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.
In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds
An HMM is initially trained with the normal behavior 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.
We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature
EXISTING SYSTEM
In case of the existing system the fraud is detected after the fraud is done that is, the fraud is detected after the complaint of the holder. And so the card holder faced a lot of trouble before the investigation finish.
And also as all the transaction is maintained in a log, we need to maintain a huge data, and also now a day’s lot of online purchase are made so we don’t know the person how is using the card online, we just capture the ip address for verification purpose
There need a help from the cyber crime to investigate the fraud.
To avoid the entire above disadvantage we propose the system to detect the fraud in a best easy way
PROPOSED SYSTEM
In this system ,we present a hidden markov model(HMM) Which does not required fraud signatures and yet is able to detect frauds by considering a cardholder’s spending habit.
Card transaction processing sequence by the stochastic process of an HMM. The details of items purchased in individual transactions are usually not known to an Fraud Detection System (FDS) running at the bank that issues credit cards to the cardholder.
Hence, we feel that HMM is an ideal choice for addressing this problem.
An FDS runs at a credit card issuing bank. Each incoming transaction is submitted to the FDS for verification. FDS receives the card details and the values if purchases to verify, whether the transaction is genuine or not.
The types of goods that are bought in that transaction are not known to the FDS.
It tries to find anomaly in the transaction based on the spending profile of the cardholder, shipping address and billing addresses.
If the FDS confirms the transaction to be
malicious, it raises an alarm, and the issuing bank declines the transaction.
The concerned cardholder may then be contacted and alerted about the possibility that the card is compromised.
ADVANTAGES
The detection of the fraud use of the card is found much faster that existing system.
In case of the existing system even the original card holder is also checked for fraud detection. But in this system no need check the original user as we maintain a log.
