27-07-2011, 10:49 AM
1 INTRODUCTION
THE 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 onlywayto 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.
2 RELATED WORK ON CREDIT CARD FRAUD DETECTION
Credit card fraud detection has drawn a lot of research
interest and a number of techniques, with special emphasis
on data mining and neural networks, have been suggested.
Ghosh and Reilly [4] have proposed credit card fraud
detection with a neural network. They have built a detection
system, which is trained on a large sample of labeled credit
card account transactions. These transactions contain example
fraud cases due to lost cards, stolen cards, application
fraud, counterfeit fraud, mail-order fraud, and nonreceived
issue (NRI) fraud. Recently, Syeda et al. [5] have used parallel
granular neural networks (PGNNs) for improving the speed
of data mining and knowledge discovery process in credit
card fraud detection. A complete system has been implemented
for this purpose. Stolfo et al. [6] suggest a credit card
fraud detection system (FDS) using metalearning techniques
to learn models of fraudulent credit card transactions.
Metalearning is a general strategy that provides a means for
combining and integrating a number of separately built
classifiers or models.
Download full report
http://googleurl?sa=t&source=web&cd=6&ve...cfdhmm.pdf&ei=W58vToPQCOfkiAK9xswr&usg=AFQjCNEnTmtD84iI5WFQsSLmYZ1mUKGaGg&sig2=N2WfaAimYWuksSa69yA60g
THE 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 onlywayto 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.
2 RELATED WORK ON CREDIT CARD FRAUD DETECTION
Credit card fraud detection has drawn a lot of research
interest and a number of techniques, with special emphasis
on data mining and neural networks, have been suggested.
Ghosh and Reilly [4] have proposed credit card fraud
detection with a neural network. They have built a detection
system, which is trained on a large sample of labeled credit
card account transactions. These transactions contain example
fraud cases due to lost cards, stolen cards, application
fraud, counterfeit fraud, mail-order fraud, and nonreceived
issue (NRI) fraud. Recently, Syeda et al. [5] have used parallel
granular neural networks (PGNNs) for improving the speed
of data mining and knowledge discovery process in credit
card fraud detection. A complete system has been implemented
for this purpose. Stolfo et al. [6] suggest a credit card
fraud detection system (FDS) using metalearning techniques
to learn models of fraudulent credit card transactions.
Metalearning is a general strategy that provides a means for
combining and integrating a number of separately built
classifiers or models.
Download full report
http://googleurl?sa=t&source=web&cd=6&ve...cfdhmm.pdf&ei=W58vToPQCOfkiAK9xswr&usg=AFQjCNEnTmtD84iI5WFQsSLmYZ1mUKGaGg&sig2=N2WfaAimYWuksSa69yA60g
