14-05-2012, 05:23 PM
Analysis on Credit Card Fraud Detection Methods
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Abstract
Due to the rise and rapid growth of E-Commerce, use
of credit cards for online purchases has dramatically increased
and it caused an explosion in the credit card fraud. 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 real life, fraudulent transactions are scattered with
genuine transactions and simple pattern matching techniques are
not often sufficient to detect those frauds accurately.
Implementation of efficient fraud detection systems has thus
become imperative for all credit card issuing banks to minimize
their losses. Many modern techniques based on Artificial
Intelligence, Data mining, Fuzzy logic, Machine learning,
Sequence Alignment, Genetic Programming etc., has evolved in
detecting various credit card fraudulent transactions. A clear
understanding on all these approaches will certainly lead to an
efficient credit card fraud detection system. This paper presents a
survey of various techniques used in credit card fraud detection
mechanisms and evaluates each methodology based on certain
design criteria.
Index Terms—Electronic Commerce, Credit card fraud,
Artificial Intelligence, Artificial Neural Networks, Sequence
Alignment, Machine Learning.
I. INTRODUCTION
The Credit Card Is A Small Plastic Card Issued To Users As A
System Of Payment. It Allows Its Cardholder To Buy Goods
And Services Based On The Cardholder's Promise To Pay For
These Goods And Services. Credit Card Security Relies On
The Physical Security Of The Plastic Card As Well As The
Privacy Of The Credit Card Number. Globalization And
Increased Use Of The Internet For Online Shopping Has
Resulted In A Considerable Proliferation Of Credit Card
Transactions Throughout The World. Thus A Rapid Growth In
The Number Of Credit Card Transactions Has Led To A
Substantial Rise In Fraudulent Activities. Credit Card Fraud Is
A Wide-Ranging Term For Theft And Fraud Committed Using
A Credit Card As A Fraudulent Source Of Funds In A Given
Transaction. Credit Card Fraudsters Employ A Large Number
Of Techniques To Commit Fraud. To Combat The Credit Card
Fraud Effectively, It Is Important To First Understand The
Mechanisms Of Identifying A Credit Card Fraud. Over The
Years Credit Card Fraud Has Stabilized Much Due To
Various Credit Card Fraud Detection And Prevention
Mechanisms.
II. RELATED WORKS
Fraud detection involves monitoring the behavior of users in
order to estimate, detect, or avoid undesirable behavior. To
counter the credit card fraud effectively, it is necessary to
understand the technologies involved in detecting credit card
frauds and to identify various types of credit card frauds [20]
[21] [22] . There are multiple algorithms for credit card fraud
detection [21] [29]. They are artificial neural-network models
which are based upon artificial intelligence and machine
learning approach [5] [7] [9] [10] [16], distributed data mining
systems [17] [19], sequence alignment algorithm which is
based upon the spending profile of the cardholder [1] [6],
intelligent decision engines which is based on artificial
intelligence [23], Meta learning Agents and Fuzzy based
systems [4]. The other technologies involved in credit card
fraud detection are Web Services-Based Collaborative Scheme
for Credit Card Fraud Detection in which participant banks can
share the knowledge about fraud patterns in a heterogeneous
and distributed environment to enhance their fraud detection
capability and reduce financial loss [8] [13], Credit Card Fraud
Detection with Artificial Immune System [13] [26],
CARDWATCH: A Neural Network Based Database Mining
System for Credit Card Fraud Detection [18] which is bases
upon data mining approach [17] and neural network models,
the Bayesian Belief Networks [25] which is based upon
artificial intelligence and reasoning under uncertainty will
counter frauds in credit cards and also used in intrusion
detection [26], case-based reasoning for credit card fraud
detection [29], Adaptive Fraud Detection which is based on
Data Mining and Knowledge Discovery [27], Real-time credit
card fraud using computational intelligence [28], and Credit
card fraud detection using self-organizing maps [30]. Most of
the credit card fraud detection systems mentioned above are
based on artificial intelligence, Meta learning and pattern
matching.
This paper compares and analyzes some of the good
techniques that have been used in detecting credit card fraud. It
focuses on credit card fraud detection methods like Fusion of
Dempster Shafer and Bayesian learning [2][5][12][15][25],
Hidden Markov Model [3], Artificial neural networks and
Bayesian Learning approach [5][25],BLAST and SSAHA
Hybridization[1][6][11][14][24], Fuzzy Darwinian System[4].
Section II gives an overview about those techniques. Section
III presents a comparative survey of those techniques and
section IV summarizes the fraud detection techniques.
A. A fusion approach using Dempster–Shafer theory and Bayesian learning
FDS of Dempster–Shafer theory and Bayesian learning
Dempster–Shafer theory and Bayesian learning is a hybrid
approach for credit card fraud detection [2][5][12][15] which
combines evidences from current as well as past behavior.
Every cardholder has a certain type of shopping behavior,
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which establishes an activity profile for them. This approach
proposes a fraud detection system using information fusion
and Bayesian learning of so as to counter credit card fraud.
Figure 1. Block diagram of the proposed fraud detection system
The FDS system consists of four components,
namely, rule-based filter, Dempster–Shafer adder, transaction
history database and Bayesian learner. In the rule-based
component, the suspicion level of each incoming transaction
based on the extent of its deviation from good pattern is
determined. Dempster–Shafer’s theory is used to combine
multiple such evidences and an initial belief is computed. Then
the initial belief values are combined to obtain an overall belief
by applying Dempster–Shafer theory. The transaction is
classified as suspicious or suspicious depending on this initial
belief. Once a transaction is found to be suspicious, belief is
further strengthened or weakened according to its similarity
with fraudulent or genuine transaction history using Bayesian
learning.
It has high accuracy and high processing Speed. It improves
detection rate and reduces false alarms and also it is applicable
in E-Commerce. But it is highly expensive and its processing
Speed is low.
B. BLAST-SSAHA Hybridization for Credit Card Fraud Detection
BLAST-SSAHA in credit card fraud detection
The Hybridization of BLAST and SSAHA algorithm
[1][6][14] is refereed as BLAH-FDS algorithm. Sequence
alignment becomes an efficient technique for analyzing the
spending behavior of customers. BLAST and SSAHA are the
efficient sequent alignment algorithms used for credit card
fraud detection.
BLAH-FDS is a two-stage sequence alignment algorithm in
which a profile analyzer (PA) determines the similarity of an
incoming sequence of transactions on a given credit card with
the genuine cardholder’s past spending sequences. The unusual
transactions traced by the profile analyzer are passed to a
deviation analyzer (DA) for possible alignment with past
fraudulent behavior. The final decision about the nature of a
transaction is taken on the basis of the observations by these
two analyzers.
BLAST-SSAHA Hybridization
When a transaction is carried out, the incoming sequence is
merged into two sequences time-amount sequence TA. The TA
is aligned with the sequences related to the credit card in CPD.
This alignment process is done using BLAST.
Figure 2. Architecture of BLAST and SSAHA Fraud Detection System
SSAHA algorithm [9] is used to improve the speed of the
alignment process. If TA contains genuine transaction, then it
would align well with the sequences in CPD. If there is any
fraudulent transactions in TP, mismatches can occur in the
alignment process. This mismatch produces a deviated
sequence D which is aligned with FHD. A high similarity
between deviated sequence D and FHD confirms the presence
of fraudulent transactions. PA evaluates a Profile score (PS)
according to the similarity between TA and CPD. DA
evaluates a deviation score (DS) according to the similarity
between D and FHD. The FDM finally raises an alarm if the
total score (PS - DS) is below the alarm threshold (AT).
The performance of BLAHFDS is good and it results in high
accuracy. At the same time, the processing speed is fast
enough to enable on-line detection of credit card fraud. It
Counter frauds in telecommunication and banking fraud
detection. But it does not detect cloning of credit cards
C. Credit Card Fraud Detection using Hidden Markov Model Hidden Markov Model
A Hidden Markov Model is a double embedded stochastic
process with used to model much more complicated stochastic
processes as compared to a traditional Markov model. If an
incoming credit card transaction is not accepted by the trained
Hidden Markov Model with sufficiently high probability, it is
considered to be fraudulent transactions.
Use Of HMM For Credit Card Fraud Detection
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Figure 3. Process Flow of the Proposed FDS
A Hidden Markov Model [3] is initially trained with the
normal behavior of a cardholder. 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. If the FDS confirms the
transaction to be malicious, it raises an alarm and the issuing
bank declines the transaction. The concerned cardholder may
then be contacted and alerted about the possibility that the card
is compromised.
HMM never check the original user as it maintains a log. The
log which is maintained will also be a proof for the bank for
the transaction made. HMM reduces the tedious work of an
employee in bank since it maintains a log. HMM produces
high false alarm as well as high false positive.
D. Fuzzy Darwinian Detection of Credit Card Fraud
The Evolutionary-Fuzzy System
Fuzzy Darwinian Detection system [4] uses genetic
programming to evolve fuzzy logic rules capable of classifying
credit card transactions into “suspicious” and “non-suspicious”
classes. It describes the use of an evolutionary-fuzzy system
capable of classifying suspicious and non-suspicious credit
card transactions.The system comprises of a Genetic
Programming (GP) search algorithm and a fuzzy expert
system.
Data is provided to the FDS system. The system first clusters
the data into three groups namely low, medium and high. The
GPThe genotypes and phenotypes of the GP System consist of
rules which match the incoming sequence with the past
sequence. Genetic Programming is used to evolve a series of
variable-length fuzzy rules which characterize the differences
between classes of data held in a database. The system is being
developed with the specific aim of insurance-fraud detection
which involves the challenging task of classifying data into the
categories: "safe" and "suspicious". When the customer’s
payment is not overdue or the number of overdue payment is
less than three months, the transaction is considered as “nonsuspicious”,
otherwise it is considered as “suspicious”. The
Fuzzy Darwinian detects suspicious and non -suspicious data
and it easily detects stolen credit card Frauds.
Figure 4. Block diagram of the Evolutionary-fuzzy system
The complete system is capable of attaining good accuracy
and intelligibility levels for real data. It has very high accuracy
and produces a low false alarm, but it is not applicable in
online transactions and it is highly expensive. The processing
speed of the system is low.
E. Credit Card Fraud Detection Using Bayesian and Neural Networks
The credit card fraud detection using Bayesian and Neural
Networks are automatic credit card fraud detection system by
means of machine learning approach. These two machine
learning approaches are appropriate for reasoning under
uncertainty.
An artificial neural network [5][7][9][10][16] consists of an
interconnected group of artificial neurons and the commonly
used neural networks for pattern classification is the feedforward
network. It consist of three layers namely input,
hidden and output layers. The incoming sequence of
transactions passes from input layer through hidden layer to
the output layer. This is known as forward propagation. The
ANN consists of training data which is compared with the
incoming sequence of transactions. The neural network is
initially trained with the normal behavior of a cardholder. The
suspicious transactions are then propagated backwards through
the neural network and classify the suspicious and nonsuspicious
transactions. Bayesian networks are also known as
belief networks and it is a type of artificial intelligence
programming that uses a variety of methods, including
machine learning algorithms and data mining, to create layers
of data, or belief. By using supervised learning, Bayesian
networks are able to process data as needed, without
experimentation. Bayesian belief networks are very effective
for modeling situations where some information is already
known and incoming data is uncertain or partially unavailable.
This information or belief is used for pattern identification and
data classification.
A neural network learns and does not need to be
reprogrammed. Its processing speed is higher than BNN.
Neural network needs high processing time for large neural
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networks. Bayesian networks are supervised algorithms and
they provide a good accuracy, but it needs training of data to
operate and requires a high processing speed.