16-01-2013, 10:11 AM
Study of Hidden Markov Model in Credit Card Fraudulent Detection
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ABSTRACT
The most accepted payment mode is credit card for both online
and offline in today’s world, it provides cashless shopping at
every shop in all countries. It will be the most convenient way to
do online shopping, paying bills etc. Hence, risks of fraud
transaction using credit card has also been increasing. In the
existing credit card fraud detection business processing system,
fraudulent transaction will be detected after transaction is done. It
is difficult to find out fraudulent and regarding loses will be
barred by issuing authorities. Hidden Markov Model is the
statistical tools for engineer and scientists to solve various
problems. In this paper, it is shown that credit card fraud can be
detected using Hidden Markov Model during transactions. Hidden
Markov Model helps to obtain a high fraud coverage combined
with a low false alarm rate.
INTRODUCTION
In day to day life credit cards are used for purchasing goods and
services by the help of virtual card for online transaction or
physical card for offline transaction. In physical transaction,
Credit cards will insert into payment machine at merchant shop to
purchase goods. Tracing fraudulent transactions in this mode may
not be possible because the attacker already steal the credit card.
The credit card company may go in financial loss if loss of credit
card is not realized by credit card holder. In online payment mode,
attackers need only little information for doing fraudulent
transaction (secure code, card number, expiration date etc.). In
this purchase method, mainly transactions will be done through
Internet or telephone. Small transactions are generally undergo
less verification, and are less likely to be checked by either the
card issuer or the merchant. Card issuers must take more
precaution against fraud detection and financial losses. Credit card
fraud cases are increasing every year. In 2008, number of
fraudulent through credit card had increased by 30 percent
because of various ambiguities in issuing and managing credit
cards. Credit card fraudulent is approximately 1.2% of the total
transaction amount, although it is not small amount as compare to
total transaction amount which is in trillions of dollars in 2007[1-
3].
HIDDEN MARKOV MODEL
A Hidden Markov Model is a finite set of states; each state is
linked with a probability distribution. Transitions among these
states are governed by a set of probabilities called transition
probabilities. In a particular state a possible outcome or
observation can be generated which is associated symbol of
observation of probability distribution. It is only the outcome, not
the state that is visible to an external observer and therefore states
are ``hidden'' to the outside; hence the name Hidden Markov
Model [5-7].
MODEL DESCRIPTION
In existing models, the bank is verified credit card
information, CVV number, Date of expiry etc., but all these
information are available on the card itself. Nowadays, bank is
also requesting to register your credit card for online secure
password. In this new model, after feeding details of card at
merchant site, then it will transfer to a secure gateway which is
established at bank’s own server. But, it is not verifying that the
transaction is fraudulent or not. If hackers will get secure code of
credit card by phishing sites or any other source, then it is very
difficult to trace fraudulent transaction.
In proposed model based on HMM will help to verify
fraudulent of transaction during transaction will be going to
happen. It includes two modules are as follow
I) Online Shopping
It comprises with many steps, first is to login into a
particular site to purchase goods or services, then choose an item
and next step is to go to payment mode where credit card
information will be required. After filling all these information,
now the page will be directed to proposed fraud detection system
which will be installed at bank’s server or merchant site.
CONCLUSION
In this paper, it has been discussed that how Hidden Markov
Model will facilitate to stop fraudulent online transaction through
credit card. The Fraud Detection System is also scalable for
handling vast volumes of transactions processing. The HMMbased
credit card fraud detection system is not taking long time
and having complex process to perform fraud check like the
existing system and it gives better and fast result than existing
system. The Hidden Markov Model makes the processing of
detection very easy and tries to remove the complexity.
At the initial state HMM checks the upcoming transaction is
fraudulent or not and it allow to accept the next transaction or not
based on the probability result. The different ranges of transaction
amount like low group, medium group, and high group as the
observation symbols were considered. The types of item have
been considered to be states of the Hidden Markov Model. It is
recommended that a technique for finding the spending behavioral
habit of cardholders, also the application of this knowledge in
deciding the value of observation symbols and initial estimation
of the model parameters