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 card 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 days
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. So 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 and easy
way.
PROPOSED SYSTEM
In proposed system, we present a Hidden Markov Model
(HMM) Which does not require 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 any Fraud Detection
System(FDS) running at the bank that
issues credit cards to the cardholders. Hence, we feel that HMM is an ideal
choice for addressing this problem. Another important advantage of the
HMM-based approach is a drastic reduction in the number of False Positives
transactions identified as malicious by an FDS although they are actually
genuine. 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
value of purchase 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 any anomaly in the transaction based on the spending profile of
the cardholder, shipping address, and billing address, etc. If the FDS confirms
the transaction to be of fraud, it raises an alarm, and the issuing bank
declines the transaction.
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