CHAPTER ONE 1.0 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 the 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[ 2-4] . Hidden Markov Model will be helpful to find out the fraudulent transaction by using spending profiles of user. It works on the user spending profiles which can be divided into major three types such as 1) Lower profile; 2) Middle profile; and 3) Higher profile. For every credit card, the spending profile is different, so it can figure out an inconsistency of user profile and try to find fraudulent transaction. It keeps record of spending profile of the card holder by both way, either offline or online. Thus analysis of purchased commodities of cardholder will be a useful tool in fraud detection system and it is assuring way to check fraudulent transaction, although fraud detection system does not keep records of number of purchased goods and categories. Every user represented by specific patterns of set which containing information about last 10 transaction using credit card [ 5-11]. The set of information contains spending profile of card holder, money spent in every transaction, the last purchase time, category of purchase etc. The potential threat for fraud 1
detection will be a deviation from set of patterns.
1.0 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 [ 6-8]. Hence, Hidden Markov Model is a perfect solution for addressing detection of fraud transaction through credit card. One more important benefit of the HMM-based approach is an extreme decrease in the number of False Positives transactions recognized as malicious by a fraud detection system even though they are really genuine [ 9]. In this prediction process, HMM consider mainly three price value ranges such as [12-13] Low (l), Medium (m) and, High (h).
First, it will be required to find out transaction amount belongs to a particular category either it will be in low, medium, or high ranges 1.2 CREDIT CARD FRAUD DETECTION USING HMM In this section, it is shown that system of credit card fraud detection based on Hidden Markov Model, which does not require fraud signatures and still it is capable to detect frauds just by bearing in mind a cardholder‟s spending habit . The particulars of purchased...
References: http://en.wikipedia.org/wiki/Adobe_Dreamweaver[Nov. 22, 2010]
 Jay Greenspan and Brad Bulger
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