Credit Card Fraud Detection

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VOL. 5,

NO. 1,



Credit Card Fraud Detection Using Hidden Markov Model
Abhinav Srivastava, Amlan Kundu, Shamik Sural, Senior Member, IEEE, and Arun K. Majumdar, Senior Member, IEEE 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. Index Terms—Internet, online shopping, credit card, e-commerce security, fraud detection, Hidden Markov Model.



popularity of online shopping is growing day by day. According to an ACNielsen study conducted in 2005, one-tenth of the world’s population is shopping online [1]. Germany and Great Britain have the largest number of online shoppers, and credit card is the most popular mode of payment (59 percent). About 350 million transactions per year were reportedly carried out by Barclaycard, the largest credit card company in the United Kingdom, toward the end of the last century [2]. Retailers like Wal-Mart typically handle much larger number of credit card transactions including online and regular purchases. As the number of credit card users rises world-wide, the opportunities for attackers to steal credit card details and, subsequently, commit fraud are also increasing. The total credit card fraud in the United States itself is reported to be $2.7 billion in 2005 and estimated to be $3.0 billion in 2006, out of which $1.6 billion and $1.7 billion, respectively, are the estimates of online fraud [3]. Credit-card-based purchases can be categorized into two types: 1) physical card and 2) virtual card. In a physical-cardbased purchase, the cardholder presents his card physically to a merchant for making a payment. To carry out fraudulent transactions in this kind of purchase, an attacker has to steal the credit card. If the cardholder does not realize the loss of card, it can lead to a substantial financial loss to the credit card company. In the second kind of purchase, only some important information about a card (card number, expiration date, secure code) is required to make the payment. Such purchases are normally done on the Internet or over the telephone. To commit fraud in these types of purchases, a fraudster simply needs to know the card details. Most of the

time, the genuine cardholder is not aware that someone else has seen or stolen his card information. The only way to detect this kind of fraud is to analyze the spending patterns on every card and to figure out any inconsistency with respect to the “usual” spending patterns. Fraud detection based on the analysis of existing purchase data of cardholder is a promising way to reduce the rate of successful credit card frauds. Since humans tend to exhibit specific behavioristic profiles, every cardholder can be represented by a set of patterns containing information about the typical purchase category, the time since the last purchase, the amount of money spent, etc. Deviation from such patterns is a potential threat to the system. Several techniques for the detection of credit card fraud have been proposed in the last few years. We briefly review some of them in Section 2.

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