International Journal of Computer Applications (0975 – 8887)
Volume 20– No.5, April 2011
33
Study of Hidden Markov Model in Credit Card
Fraudulent Detection
V. Bhusari S. Patil
College of Computer Engineering, College of Computer Engineering,
Bharati Vidhyapeeth University, Bharati Vidhyapeeth University,
Pune-411043, India Pune-411043, India
ABSTRACT
The most accepted payment mode is credit card for both online
and offline in today’s world, it provides cashless shopping at
every shop in all countries. It will be the most convenient way to
do online shopping, paying bills etc. Hence, risks of fraud
transaction using credit card has also been increasing. In the
existing credit card fraud detection business processing system,
fraudulent transaction will be detected after transaction is done. It
is difficult to find out fraudulent and regarding loses will be
barred by issuing authorities. Hidden Markov Model is the
statistical tools for engineer and scientists to solve various
problems. In this paper, it is shown that credit card fraud can be
detected using Hidden Markov Model during transactions. Hidden
Markov Model helps to obtain a high fraud coverage combined
with a low false alarm rate.
Keywords: Hidden Markov Model, fraud transaction, credit
card.
1. 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
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[1-
3].
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 [4, 10]. 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 detection will be a deviation from set of patterns.
2. 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 [5-7].
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 [8].
In this prediction process, HMM consider mainly three price
value ranges such as [14-15]
1) Low (l),
2) Medium (m) and,
3) 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.
3. 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 [9]. The particulars
of purchased items in single transactions are generally unknown
to any Credit card Fraud Detection System running either at the
International Journal of Computer Applications (0975 – 8887)
Volume 20– No.5, April 2011
34
bank that issues credit cards to the cardholders or at the merchant
site where goods is going to be purchased [13].
As business processing of credit card fraud detection system
runs on a credit card issuing bank site or merchant site. Each
arriving transaction is submitted to the fraud detection system for
verification purpose [12]. The fraud detection system accept the
card details such as credit card number, cvv number, card type,
expiry date and the amount of items purchase to validate, whether
the transaction is genuine or not [13].
The implementation techniques of Hidden Markov Model in
order to detect fraud transaction through credit cards, it create
clusters of training set and identify the spending profile of
cardholder [11]. The number of items purchased, types of items
that are bought in a particular transaction are not known to the
Fraud Detection system, but it only concentrates on the amount of
item purchased and use for further processing [15]. It stores data
of different amount of transactions in form of clusters depending
on transaction amount which will be either in low, medium or
high value ranges.
It tries to find out any variance in the transaction based on
the spending behavioral profile of the cardholder, shipping
address, and billing address and so on [10]. The probabilities of
initial set have chosen based on the spending behavioral profile of
card holder and construct a sequence for further processing. If the
fraud detection system makes sure that the transaction to be of
fraudulent, it raises an alarm, and the issuing bank declines the
transaction [12].
For the security purpose, the Security information module
will get the information features and its store’s in database [8].
If the card lost then the Security information module form
arises to accept the security information. The security form has a
number of security questions like account number, date of birth,
mother name, other personal question and their answer, etc. where
the user has to answer it correctly to move to the transaction
section [9]. All these information must be known by the card
holder only. It has informational privacy and informational self-
determination that are addressed evenly by the innovation
affording people and entities a trusted means to user, secure,
search, process, and exchange personal and/or confidential
information [11].
The system and tools for pre-authorizing business provided
that a connections tool to a retailer and a credit card owner [14].
The cardholder initiates a credit card transaction processing by
communicating to a credit card number, card type with expiry
date and storing it into database, a distinctive piece of information
that characterizes a particular transaction to be made by an
authoritative user of the credit card at a later time [11].
The details are received as network data in the database only
if an accurate individual recognition code is used with the
communication [8]. The cardholder or other authoritative user can
then only make that particular transaction with the credit card.
Since the transaction is pre-authorized, the vendor does not need
to see or transmit an accurate individual recognition code [12].
A. Techniques and Algorithm Used
To record the credit card transaction dispensation process in
conditions of a Hidden Markov Model (HMM), it creates through
original deciding the inspection symbols in our representation. We
quantize the purchase values x into M price ranges V
1
, V
2
. . . V
M
,
form the study symbols by the side of the issuing bank [14]. The
genuine price variety for each symbol is configurable based on the
expenditure routine of personal cardholders. HMM determine
these prices rang dynamically by using clustering algorithms (like
K clustering algorithm) on the price values of every card holder
transactions. It uses cluster V
k
for clustering algorithm as k ¼ 1, 2
. . . . M, which can be represented both observations on price
value symbols as well as on price value range [13].
In this prediction process it considers mainly three price
value ranges such as 1) low (l) 2) Medium (m) and 3) High
(h)[23]. So set of this model prediction symbols is V { l, m, h}, so
V ¼ f as l (low), m (medium), h (high) which makes M ¼ 3. E.g.
If card holder perform a transaction as $ 250 and card holders
profile groups as l (low) = (0, $ 100], m (medium) = ($ 200, $
500], and h (high) = ($ 500, up to credit card limit], then
transaction which card holder want to do will come in medium
profile group. So the corresponding profile group or symbol is M
and V (2) will be used.
In various period of time, purchase of various types with the
different amount would make by credit card holder. It uses the
deviation in a purchasing amount of latest 10 transaction sequence
(and adding one new transaction in that sequence) which is one of
the possibilities related to the probability calculation [16].
In initial stage, model does not have data of last 10
transactions, in that case, model will ask to the cardholder to feed
basic information during transaction about the cardholder such as
mother name, place of birth, mailing address, email id etc. Due to
feeding of information, HMM model acquired relative data of
transaction for further verification of transaction on spending
profile of cardholder.
4. MODEL DESCRIPTION
In existing models, the bank is verified credit card
information, CVV number, Date of expiry etc., but all these
information are available on the card itself. Nowadays, bank is
also requesting to register your credit card for online secure
password. In this new model, after feeding details of card at
merchant site, then it will transfer to a secure gateway which is
established at bank’s own server. But, it is not verifying that the
transaction is fraudulent or not. If hackers will get secure code of
credit card by phishing sites or any other source, then it is very
difficult to trace fraudulent transaction.
In proposed model based on HMM will help to verify
fraudulent of transaction during transaction will be going to
happen. It includes two modules are as follow
I) Online Shopping
It comprises with many steps, first is to login into a
particular site to purchase goods or services, then choose an item
and next step is to go to payment mode where credit card
information will be required. After filling all these information,
now the page will be directed to proposed fraud detection system
which will be installed at bank’s server or merchant site.
II) Fraud Detection System
All the information about credit card (Like Credit card
number, credit card CVV number, credit card Expiry month and
year, name on credit card etc.) will be checked with credit card
database. If User entered database is correct then it will ask
Personal Identity number (PIN). After matching of Personal
International Journal of Computer Applications (0975 – 8887)
Volume 20– No.5, April 2011
35
Identity number (PIN) with database and account balance of
user’s credit card is more than the purchase amount, the fraud
checking module will be activated.
The verification of all data will be checked before the first
page load of credit card fraud detection system.
If user credit card has less than 10 transactions then it will
directly ask to provide personal information to do the transaction.
Once database of 10 transactions will be developed, then fraud
detection system will start to work.
By using this observation, determine users spending profile.
The purchase amount will be checked with spending profile of
user. By transition probabilistic calculation based on HMM, it
concludes whether the transaction is real or fraud. If transaction
may be concluded as fraudulent transaction then user must enter
security information. This information is related with credit card
(like account number, security question and answer which are
provided at the time of registration). If transaction will not be
fraudulent then it will direct to give permission for transaction.
If the detected transaction is fraudulent then the Security
information form will arise. It has a set of question where the user
has to answer them correctly to do the transaction. These forms
have information such as personal, professional, address; dates of
birth, etc are available in the database. If user entered information
will be matched with database information, then transaction will
be done securely. And else user transaction will be terminated and
transferred to online shopping website. The flowchart of proposed
module is shown in Figure 1.
Fig.1: Flowchart of HMM module for credit card fraudulent
detection.
5. RESULTS AND DISCUSSION
In this section, it is shown that fraud detection will be checked on
last 10 transactions and also calculate percentage of each spending
profile (low, medium and high) based on total number of
transactions. In Table 1, list of all transactions are shown.
Table 1, list of all transactions happened till date.
No. of Transaction
Amount
No. of Transaction
Amount
1st
140
11th
210
2nd
125
12th
550
3rd
15
13th
800
4th
5
14th
110
5th
10
15th
35
6th
125
16th
118
7th
15
17th
20
8th
120
18th
148
9th
10
19th
141
10th
280
20th
6
The most recent transaction is placed at the first position and
correspondingly first transaction is placed at the last position in
the table.
The pattern of spending profile of the card holder is shown in
Figure 2 based on all transactions done.
Fig. 2: Spending profile of all transactions.
The percentage calculation of each spending profile (low, medium
and high) of the card holder based on price distribution range as
mentioned earlier is shown in Figure 3.
Fig. 3: Percentage of each spending profile.
Start
Login
Purchase
Credit Card Information
Verification
Fraud Check
Transaction
Stop
International Journal of Computer Applications (0975 – 8887)
Volume 20– No.5, April 2011
36
It has been noticed that medium spending profile has maximum
percentage of 50, followed by low profile 40% and then 10% of
high spending profile as per details of transactions in Table 1.
Fraud detection mean distribution is shown in Figure 4, where
probability of false transaction compared with that of genuine
transaction.
Fig 4: Probability of False Alarm compared with Fraud
Transaction Mean Distribution.
In Figure 4, it is noted that when probability of genuine
transaction is going down correspondingly probability of false
transaction is going to increase and vise versa. It helps to find out
the false alarm for the detection of fraud transaction. Hence, when
the probability of false alarm will be more than threshold
probability, then it will generate an alarm for fraudulent and also
decline the transaction.
6. CONCLUSION
In this paper, it has been discussed that how Hidden Markov
Model will facilitate to stop fraudulent online transaction through
credit card. The Fraud Detection System is also scalable for
handling vast volumes of transactions processing. The HMM-
based credit card fraud detection system is not taking long time
and having complex process to perform fraud check like the
existing system and it gives better and fast result than existing
system. The Hidden Markov Model makes the processing of
detection very easy and tries to remove the complexity.
At the initial state HMM checks the upcoming transaction is
fraudulent or not and it allow to accept the next transaction or not
based on the probability result. The different ranges of transaction
amount like low group, medium group, and high group as the
observation symbols were considered. The types of item have
been considered to be states of the Hidden Markov Model. It is
recommended that a technique for finding the spending behavioral
habit of cardholders, also the application of this knowledge in
deciding the value of observation symbols and initial estimation
of the model parameters
In our proposed model, we have found out more than 84%
transactions are genuine and very low false alarm which is about 7
% of total number of transactions.
The relative studies and our results sure that the correctness
and effectiveness of the proposed system is secure to 80 percent
over a broad deviation in the input data.
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