JAVA- Analysis on Credit Card Fraud Detection Methods\Base paper

International Conference on Computer, Communication and Electrical Technology – ICCCET2011, 18th & 19th March, 2011
978-1-4244-9394-4/11/$26.00 ©2011 IEEE
152
Analysis on Credit Card Fraud Detection Methods
1S. Benson Edwin Raj, 2A. Annie Portia
1Assistant Professor (SG), P.G., 2Scholar
Department of CSE
Karunya University, Coimbatore
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,
International Conference on Computer, Communication and Electrical Technology – ICCCET2011, 18th & 19th March, 2011
153
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
International Conference on Computer, Communication and Electrical Technology – ICCCET2011, 18th & 19th March, 2011
154
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
International Conference on Computer, Communication and Electrical Technology – ICCCET2011, 18th & 19th March, 2011
155
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.
III. COMPARISON OF VARIOUS FRAUD DETECTION SYSTEMS
PARAMETERS USED FOR COMPARISON
The Parameters used for comparison of various Fraud
Detection Systems are Accuracy, Fraud Detection Rate in
terms of True Positive and false positive, cost and training
required, Supervised Learning. The comparison performed is
shown in Table 1.
Accuracy: It represents the fraction of total number of
transactions (both genuine and fraudulent) that have been
detected correctly.
Method: It describes the methodology used to counter the
credit card fraud. The efficient methods like sequence
alignment, machine learning, neural networks are used to
detect and counter frauds in credit card transactions.
True Positive (TP): It represents the fraction of fraudulent
transactions correctly identified as fraudulent and genuine
transactions correctly identified as genuine. False Positive
(FP): It represents fraction of genuine transactions identified as
fraudulent and fraudulent transactions identified as
genuine.Training data: It consists of a set of training examples.
The fraud detection systems are initially trained with the
normal behavior of a cardholder.
Supervised Learning: It is the machine learning task of
inferring a function from supervised training data. Comparison
Results
The Comparison table was prepared in order to compare
various credit card fraud detection mechanisms. All the
techniques of credit card fraud detection described in the table
1 have its own strengths and weaknesses.
Results show that the fraud detection systems such as Fuzzy
Darwinian, Dempster and Bayesian theory have very high
accuracy in terms of TP and FP. At the same time, the
processing speed is fast enough to enable on-line detection of
credit card fraud in case of BLAH-FDS and ANN.
IV. CONCLUSION
Efficient credit card fraud detection system is an utmost
requirement for any card issuing bank. Credit card fraud
detection has drawn quite a lot of interest from the research
community and a number of techniques have been proposed to
counter credit fraud. The Fuzzy Darwinian fraud detection
systems improve the system accuracy. Since The Fraud
detection rate of Fuzzy Darwinian fraud detection systems in
terms of true positive is 100% and shows good results in
detecting fraudulent transactions. The neural network based
CARDWATCH shows good accuracy in fraud detection and
Processing Speed is also high, but it is limited to one-network
per customer. The Fraud detection rate of Hidden Markov
model is very low compare to other methods. The hybridized
algorithm named BLAH-FDS identifies and detects fraudulent
transactions using sequence alignment tool. The processing
speed of BLAST-SSAHA is fast enough to enable on-line
detection of credit card fraud. BLAH-FDS can be effectively
used to counter frauds in other domains such as
telecommunication and banking fraud detection. The ANN and
BNN are used to detect cellular phone fraud, Network
Intrusion. All the techniques of credit card fraud detection
discussed in this survey paper have its own strengths and
weaknesses. Such a survey will enable us to build a hybrid
approach for identifying fraudulent credit card transactions.
REFERENCES
[1] Amlan Kundu, Suvasini Panigrahi, Shamik Sural and Arun K.
Majumdar, “BLAST-SSAHA Hybridization for Credit Card Fraud
Detection,” IEEE Transactions On Dependable And Secure Computing,
vol. 6, Issue no. 4, pp.309-315, October-December 2009.
[2] Amlan Kundu, Suvasini Panigrahi, Shamik Sural and Arun K.
Majumdar, “Credit card fraud detection: A fusion approach using
Dempster–Shafer theory and Bayesian learning,” Special Issue on
Information Fusion in Computer Security, Vol. 10, Issue no 4, pp.354-
363, October 2009.
[3] Abhinav Srivastava, Amlan Kundu, Shamik Sural, Arun K.
Majumdar, “Credit Card Fraud Detection using Hidden Markov Model,”
IEEE Transactions On Dependable And Secure Computing, vol. 5, Issue
no. 1, pp.37-48, January-March 2008.
[4] Peter J. Bentley, Jungwon Kim, Gil-Ho Jung and Jong-Uk Choi,
“Fuzzy Darwinian Detection of Credit Card Fraud,” In the 14th Annual
Fall Symposium of the Korean Information Processing Society, 14th
October 2000.
[5] Sam Maes, Karl Tuyls, Bram Vanschoenwinkel, Bernard Manderick,
“Credit card fraud detection using Bayesian and neural networks,”
Interactive image-guided neurosurgery, pp.261-270, 1993.
[6] Amlan Kundu, S. Sural, A.K. Majumdar, “Two-Stage Credit Card
Fraud Detection Using Sequence Alignment,” Lecture Notes in
Computer Science, Springer Verlag, Proceedings of the International
Conference on Information Systems Security, Vol. 4332/2006, pp.260-
275, 2006.
[7] Simon Haykin, “Neural Networks: A Comprehensive Foundation,”
2nd Edition, pp.842, 1999.
International Conference on Computer, Communication and Electrical Technology – ICCCET2011, 18th & 19th March, 2011
156
[8] A. Chiu, C. Tsai, “A Web Services-Based Collaborative Scheme for
Credit Card Fraud Detection,” Proceedings of the IEEE International
Conference on e-Technology, e-Commerce and e-Service, pp.177-181,
2004.
[9] R. Brause, T. Langsdorf, M. Hepp, “Neural Data Mining for Credit
Card
Fraud Detection, “International Conference on Tools with Artificial
Intelligence, pp.103-106, 1999.
[10] Ghosh, D.L. Reilly, “Credit Card Fraud Detection with a Neural-
Network,” Proceedings of the International Conference on System
Science, pp.621-630, 1994.
[11] Z. Ning, A.J. Cox, J.C. Mullikin, “SSAHA: A Fast Search Method for
Large DNA Databases,” Genome Research, Vol. 11, No. 10, pp.1725-
1729, 2001.
[12] Lam, Bacchus, “Learning bayesian belief networks: An approach
basedon the MDL principle,” Computational Intelligence, Vol. 10, Issue
No. 3, pp.269–293, August 1994.
[13] Manoel Fernando Alonso Gadi, Xidi Wang, Alair Pereira do Lago,
“Credit Card Fraud Detection with Artificial Immune System,” Lecture
Notes in Computer Science, Vol. 5132/2008, pp.119-131, 2008.
[14] Tom Madden, “The BLAST Sequence Analysis Tool”, 2003.
[15] M. Mehdi, S. Zair, A. Anou and M. Bensebti,” A Bayesian Networks in
Intrusion Detection Systems,” International Journal of Computational
Intelligence Research, Issue No. 1, pp.0973-1873 Vol. 3, 2007.
[16] Ray-I Chang, Liang-Bin Lai, Wen-De Su, Jen-Chieh Wang, Jen-Shiang
Kouh, “Intrusion Detection by Backpropagation Neural Networks with
Sample-Query and Attribute-Query,” Research IndiaPublications, pp.6-
10, November 26, 2006.
[17] C. Phua, V. Lee, K. Smith, R. Gayler, “A Comprehensive Survey of Data
Mining-based Fraud Detection Research,” Artificial Intelligence Review,
2005.
[18] E. Aleskerov, B. Freisleben, B. Rao, “CARDWATCH: A Neural
Network Based Database Mining System for Credit Card Fraud
Detection,” Proceedings of IEEE/IAFE Conference on Computational
Intelligence for Financial Engineering (CIFEr), pp.220-226, 1997.
[19] Philip K. Chan ,Wei Fan, Andreas L. Prodromidis, Salvatore J. Stolfo,
“Distributed Data Mining in Credit Card Fraud Detection,” IEEE
Intelligent Systems ISSN, Vol. 14 , Issue No. 6, Pages: 67 – 74,
November 1999.
[20] Barry Masuda, “Credit Card Fraud Prevention: A Successful Retail
Strategy,” crime prevention, Vol. 6, 1986.
[21] Linda Delamaire, Hussein Abdou, John Pointon, “Credit card fraud and
detection techniques: a review,” Banks and Bank Systems, pp. 57-68,
2009.
[22] Tej Paul Bhatla, Vikram Prabhu & Amit Dua “Understanding Credit
Card Frauds,” 2003.
[23] Russell, Norvig ,” Artificial Intelligence – A Modern Approach,” 2nd
Edition, 2003.
[24] S.F.Altschul, W. Gish, W. Miller, W. Myers, J. Lipman, “Basic Local
Alignment Search Tool,” Journal of Molecular Biology, Vol. 215,
pp.403-410, 1990.
[25] Ezawa.K. & Norton.S,”Constructing Bayesian Networks to Predict
Uncollectible Telecommunications Accounts,” IEEE Expert, October;
45-51, 1996.
[26] Fan, W. Miller, M.Stolfo, S.Lee & P Chan, “Using Artificial Anomalies
to Detect Unknown and Known Network Intrusions,” Proc. of ICDM01,
pp.504-507, 2001.