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06-01-2010, 06:49 AM
Post: #1
Credit Card Fraud Detection Using Hidden Markov Models
I am astudent of one of the leading colleges in Nigeria, and in respect to this we were ask to write a good project, i saw this this topic and i am interested in it and need assistance and also would want to add my own idea in anyway to improve the topic.

i would be very gratefull if my situation is been considered and rendered quick reply.
07-01-2010, 05:19 PM
Post: #2
RE: Credit Card Fraud Detection Using Hidden Markov Models
The use of credit cards has dramatically increased due to development in the e- commerce technology. cases of Credit card fraud are also increasing.
Credit-card-based purchases can be categorized into two
types: 1) physical card
the cardholder presents his card physically
to a merchant for making a payment.
an attacker has to steal
the credit card to show fraud in this case.


2) virtual card.
Here, card number, expiration
date, secure code or similar information are required to make the payment.
The only way to detect
his kind of fraud is to analyze the spending patterns
.

HMM(Hidden markov model) BACKGROUND

An HMM is a double embedded stochastic process with two
hierarchy levels which can be used to model much more
complicated stochastic processes as compared to a traditional
Markov model. An HMM has a finite set of states governed by
a set of transition probabilities. In a given state, an
outcome can be generated according to an
associated probability distribution. only the outcome that is visible to an external observer and not the state . Fields like speech recognition, bioinformatics, and genomics use the HMM. HMM is also used in anomaly detection. HMM was used to model human behavior.
HMM can be characterized by the following:
N is the number of states in the model. the set of states is denoted as S={S1 ; S2 ; . . . SN}
; . . . ; N is an individual state. The state at time
instant t is denoted by qt .
M is the number of distinct observation symbols per
state. The observation symbols correspond to the
physical output of the system being modeled. We
denote the set of symbols V={V1 ; V2 ; . . . VM}

USE OF HMM FOR CREDIT CARD FRAUD DETECTION
A FDS(fraud detection system) runs at a credit card issuing bank. It is sent the card details and the value of purchase to verify
whether the transaction is genuine or not and tries to find any anomaly in the transaction. This calculation is based on spending profile of the cardholder, shipping address, and
billing address, etc.If found to be fraudulent, it raises an alarm, and the issuing bank denies the transaction.

HMM Model for Credit Card Transaction Processing
A credit cardholder makes different kinds of purchases of

different amounts over a period of time.The sequence of types of
purchase is more stable compared to the sequence of
transaction amounts. The set of all possible types of purchase and,
equivalently, the set of all possible lines of business of
merchants forms the set of hidden states of the HMM. the actual items purchased
in the transaction are not determined. After deciding the state and symbol representations, the
next step is to determine the probability matrices A, B, and
mu so that representation of the HMM is complete.

Dynamic Generation of Observation Symbols

we train and maintain an HMM for each cardholder the amount
that the cardholder spent in his transactions are determined from the bank database and K-means clustering algorithm to determine the clusters. The grouping is performed by minimizing the sum of
squares of distances between each data point and the
centroid of the cluster to which it belongs.

Spending profile of cardholders
suggests his normal spending behavior. These are are determined at the
end of the clustering step.

Fraud Detection

After the HMM parameters are learned, we take the
symbols from a cardholderâ„¢s training data and form an
initial sequence of symbols. We input
this sequence to the HMM and compute the probability of
acceptance by the HMM.We input this
new sequence to the HMM formed by dropping O and calculate the probability of
acceptance by the HMM. If O is malicious, the issuing bank
does not approve the transaction, but in other cases is added in the sequence perma-nently, and the new sequence is used as the base sequence for
determining the validity of the next transaction.

Fullseminar report available in the link:
ieeexplore.ieee.org/iel5/8858/4447479/04358713.pdf

you need IEEE subscription to access it . Your instituition must be having it probably.

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04-04-2010, 03:36 PM
Post: #3
RE: Credit Card Fraud Detection Using Hidden Markov Models
please read http://www.seminarprojects.com/Thread-cr...dels--5664 and http://www.seminarprojects.com/Thread-fr...rkov-model and http://www.seminarprojects.com/Thread-cr...kov-models
and http://www.seminarprojects.com/Thread-cr...rkov-model for getting detailed information of Credit Card Fraud Detection Using Hidden Markov Models Related information

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29-09-2010, 10:39 AM
Post: #4
RE: Credit Card Fraud Detection Using Hidden Markov Models

.doc  CreditCardFraudDetectionUsingHiddenMarkovModel.doc (Size: 102.5 KB / Downloads: 589)
Credit Card Fraud Detection Using Hidden Markov Model

ABSTRACT

Purpose
The purpose of this document is to present a detailed description of the Credit-card- based purchases can be categorized into two types: 1) physical card and 2) virtual card. In a physical-card based 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.

01-10-2010, 11:39 AM
Post: #5
RE: Credit Card Fraud Detection Using Hidden Markov Models


.doc  Final Document.doc (Size: 928 KB / Downloads: 417)

Credit Card Fraud Detection Using Hidden Markov Model

Abstract
:

Now a day the usage 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
13-10-2010, 05:12 PM
Post: #6
RE: Credit Card Fraud Detection Using Hidden Markov Models

.doc  IDNS03.doc (Size: 1.49 MB / Downloads: 301)
Credit Card Fraud Detection Using
Hidden Markov Model

Abstract:

Now a day the usage 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




Introduction
Credit-card-based purchases can be categorized into two types: 1) physical card and 2) virtual card. In a physical-card based 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 behaviorist 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.

http://www.seminarprojects.com/Thread-cr...-using-hmm

http://www.seminarprojects.com/Thread-cr...del--12225
16-10-2010, 10:24 AM
Post: #7
RE: Credit Card Fraud Detection Using Hidden Markov Models
This article is presented by:Erik L.L. Sonnhammer
Gunnar von Heijne
Anders Krogh
A hidden Markov model for predicting transmembrane helices in protein
sequences




Abstract
A novel method to model and predict the location and orientation of alpha helices in membrane- spanning proteins is presented. It is based on a hidden Markov model (HMM) with an architecture that corresponds closely to the biological system. The model is cyclic with 7 types of states for helix core, helix caps on either side, loop on the cytoplasmic side, two loops for the non-cytoplasmic side, and a globular domain state in the middle of each loop. The two loop paths on the non-cytoplasmic side are used to model short and long loops separately, which corresponds biologically to the two known different membrane insertions mechanisms. The close mapping between the biological and computational states allows us to infer which parts of the model architecture are important to capture the information that encodes the membrane topology, and to gain a better understanding of the mechanisms and constraints involved. Models were estimated both by maximum likelihood and a discriminative method, and a method for reassignment of the membrane helix boundaries were developed. In a cross validated test on single sequences, our transmembrane HMM, TMHMM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 83 proteins with known topology. The same accuracy was achieved on a larger dataset of 160 proteins. These results compare favourably with existing methods.
Introduction.
Prediction of membrane-spanning alpha helices in proteins is a frequent sequence analysis objective. A large portion of the proteins in a genome encode integral membrane proteins (Himmelreich et al. 1996; Frishman & Mewes 1997; Wallin & von Heijne 1998). Knowledge of the presence and exact location of the transmembrane helices is important for functional annotation and to direct functional analysis. Transmembrane helices are substantially easier to predict than helices in globular domains. Predicting 95% of the transmembrane helices in the ‘correct’ location is not unusual (Cserzo et al. 1997; Rost et al. 1995). By ‘correct’ is meant that the prediction overlaps the true location. The reason for this high accuracy is that most transmembrane alpha helices are encoded by an unusually long stretch of hydrophobic residues. This compositional bias is imposed by the constraint that residues buried in lipid membranes must be suitable for hydrophobic interactions with the lipids. The hydrophobic signal is so strong that a straightforward approach of calculating a propensity scale for residues in transmembrane helices and applying a sliding window with a cutoff already performs quite well. In addition to knowing the location of a transmembrane helix, knowledge of its orientation, i.e. whether it runs inwards or outwards, is also important for making functional inferences for different parts of the sequence. The orientations of the transmembrane helices give the overall topology of the protein. It is known that the positively charged residues arginine and lysine play a major role in determining the orientation as they are mainly found in non-transmembrane parts of the protein (‘loops’) on the cytoplasmic side (von Heijne 1986; Jones, Taylor, & Thornton 1994; Persson & Argos 1994; Wallin &von Heijne 1998), often referred to as the ‘positiveinside rule’. Since the rule also applies to proteins in the membrane of intracellular organelles (Gavel et al. 1991; Gavel & von Heijne 1992), we shall use the terms ‘cytoplasmic’ and ‘non-cytoplasmic’ for the two sides of a membrane. The difference in amino acid usage between cytoplasmic and non-cytoplasmic loops can be exploited to improve the prediction of transmembrane helices by validating potential transmembrane helices by the charge bias they would produce (von Heijne 1992). Despite this relatively consistent topogenic signal, correct prediction of the location and orientation of all transmembrane segments has proved to be a difficult problem. On a reasonably large dataset of single sequences, a topology accuracy of 77% has been reported (Jones, Taylor, & Thornton 1994), and aided with multiple alignments 86% (Rost, Fariselli, & Casadio 1996). The difficulty in predicting the topology seems to be partly caused by the fact that the positive-inside rule can be blurred by globular domains in loops on the non-cytoplasmic side that contain a substantial number of positively charged residues.
For more information about this article,please follow the link:
http://www.google.co.in/url?sa=t&source=...p3-E6-dL9w
23-10-2010, 07:57 PM
Post: #8
RE: Credit Card Fraud Detection Using Hidden Markov Models
PLEASE HELP ME IN THIS PROJECT !!!!
01-11-2010, 04:02 PM
Post: #9
RE: Credit Card Fraud Detection Using Hidden Markov Models

.ppt  Lecture5-hmm.ppt (Size: 1.21 MB / Downloads: 143)

Hidden Markov Models
Bonnie Dorr Christof Monz
CMSC 723: Introduction to Computational Linguistics

Hidden Markov Model (HMM)

HMMs allow you to estimate probabilities of unobserved events
Given plain text, which underlying parameters generated the surface
E.g., in speech recognition, the observed data is the acoustic signal and the words are the hidden parameters
23-11-2010, 08:33 PM
Post: #10
RE: Credit Card Fraud Detection Using Hidden Markov Models
Can u please send me the full report on this topic as soon as possible...It will be very helpfull.
24-11-2010, 09:56 AM
Post: #11
RE: Credit Card Fraud Detection Using Hidden Markov Models
hi pratik.bhatia007ia,

you can download the report from above post.
26-11-2010, 12:05 PM
Post: #12
RE: Credit Card Fraud Detection Using Hidden Markov Models
Speech Recognition with Hidden Markov Models



HMMs for Speech
Speech is the output of an HMM; problem is to find most likely model for a given speech observation sequence.

Speech is divided into sequence of 10-msec frames, one frame per state transition (faster processing). Assume speech can be recognized using 10-msec chunks.


HMMs for Speech
Each state can be associated with  sub-phoneme  phoneme
 sub-word

Usually, sub-phonemes or sub-words are used, to account for spectral dynamics (coarticulation).

One HMM corresponds to one phoneme or word

For each HMM, determine the probability of the best state sequence that results in the observed speech.

Choose HMM with best match (probability) to observed speech.

Given most likely HMM and state sequence, maybe determine the corresponding phoneme and word sequence.
7-state word model for “cat” with null states

Null states do not emit observations, and are entered and exited at the same time t. Theoretically, they are unnecessary. Practically, they can make implementation easier.

States don’t have to correspond directly to phonemes, but are commonly labeled using phonemes.


This permits several different models for each phoneme, depending on surrounding phonemes (context sensitive)

k-ae+t
p-ae+t
k-ae+p

Probability of “illegal” state sequence is zero (never used) sil-k+ae p-ae+t


Much larger number of states to train on… (50 vs. 125,000 for a full set of phonemes, 39 vs. 59,319 for reduced set).


For more information about this article,please follow the link:3
http://www.cslu.ogi.edu/people/hosom/cs5...speech.ppt
11-12-2010, 07:01 PM
Post: #13
RE: Credit Card Fraud Detection Using Hidden Markov Models
ieee pdf for this project
13-12-2010, 03:18 PM
Post: #14
RE: Credit Card Fraud Detection Using Hidden Markov Models
sorry , we don't have ieee pdf for this topic
26-12-2010, 09:36 PM
Post: #15
RE: Credit Card Fraud Detection Using Hidden Markov Models
Hi,
try this link for the pdf:
http://www.wiphala.net/research/proposal..._model.pdf

Please Use Search http://seminarprojects.com/search.php wisely To Get More Information About A Seminar Or Project Topic
29-01-2011, 10:22 PM
Post: #16
RE: Credit Card Fraud Detection Using Hidden Markov Models
Hello sir can u please send me the project asap.....
13-02-2011, 10:29 AM
Post: #17
Smile RE: Credit Card Fraud Detection Using Hidden Markov Models
plz snd presentation file if u hav....
15-03-2011, 02:36 PM
Post: #18
RE: Credit Card Fraud Detection Using Hidden Markov Models
Presented by:
B.SURESH
E.SOMAIAH


.ppt  PPT.ppt (Size: 3.18 MB / Downloads: 124)
CREDIT CARD FRAUD DETECTION USING HIDDEN MARKOV MODEL
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
EXISTING SYSTEM
 In case of the existing system the fraud is detected after the fraud is done that is, the fraud is detected after the complaint of the holder. And so the card holder faced a lot of trouble before the investigation finish.
 And also as all the transaction is maintained in a log, we need to maintain a huge data, and also now a day’s lot of online purchase are made so we don’t know the person how is using the card online, we just capture the ip address for verification purpose
 There need a help from the cyber crime to investigate the fraud.
 To avoid the entire above disadvantage we propose the system to detect the fraud in a best easy way
PROPOSED SYSTEM
 In this system ,we present a hidden markov model(HMM) Which does not required fraud signatures and yet is able to detect frauds by considering a cardholder’s spending habit.
 Card transaction processing sequence by the stochastic process of an HMM. The details of items purchased in individual transactions are usually not known to an Fraud Detection System (FDS) running at the bank that issues credit cards to the cardholder.
 Hence, we feel that HMM is an ideal choice for addressing this problem.
 An FDS runs at a credit card issuing bank. Each incoming transaction is submitted to the FDS for verification. FDS receives the card details and the values if purchases to verify, whether the transaction is genuine or not.
 The types of goods that are bought in that transaction are not known to the FDS.
 It tries to find anomaly in the transaction based on the spending profile of the cardholder, shipping address and billing addresses.
 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.
ADVANTAGES
 The detection of the fraud use of the card is found much faster that existing system.
 In case of the existing system even the original card holder is also checked for fraud detection. But in this system no need check the original user as we maintain a log.
22-03-2011, 02:38 PM
Post: #19
RE: Credit Card Fraud Detection Using Hidden Markov Models
Presented by:
S.Narmadha
S.Nithyakamatchi
T.Parimalam
M.Parthipan


.ppt  Credit Card Fraud Detection using hidden markov model.ppt (Size: 415.5 KB / Downloads: 168)
ABSTRACT
 The project titled “Credit card fraud detecting” is designed using Active Server Pages .NET with Microsoft Visual Studio.NET 2005 as front end and Microsoft SQL Server 2000 as back end which works in .Net framework version 2.0. The coding language used is C# .Net.
 In this project we are using hidden markov model this is mainly used to take data from the database.
 The bank ask some set of secret question to the user while providing the credit card to the new customer.
 Whenever the user swipe their credit card the admin will ask the secret question.
 The answer entered by the user is checked with the data in the database
 If the answer is correct the particular transaction is carried out or otherwise it will block the code.
EXISTING SYSTEM
Whenever a new user registers, his personal details are not cross checked.
Access is provided to him instantly. There is no credit card fraud checking.
This may allow any user to register and thus allowing malicious users also.
PROPOSED SYSTEM
In proposed system, whenever a new user registers, his credit card details are cross checked and then only his user id is generated.
This allows only correct users to login each time. At the same time credit card details are verified each time whenever a customer buys product.
This verification enables only right users to buy products.
Advantages:
 Credit card details are verified each time when a user buys products.
 Easy to use.
 Customer satisfaction is maintained.
 They will intimate to the personal mail id about the user id and password after verification.
SYSTEM SPECIFICATION
HARDWARE CONFIGURATION

 SYSTEM : PENTIUM III 700 MHZ
 HARD DISK : GB
 MONITOR : 15 VGA COLOUR MONITOR
 MOUSE : LOGITECH.
 RAM : 2 GB
 KEYBOARD : 110 KEYS ENHANCED.
SOFTWARE CONFIGURATION
 OPERATING SYSTEM : WINDOWS XP
 ENVIRONMENT : VISUAL STUDIO .NET 2005
 WEB TECHNOLOGY : ACTIVE SERVER PAGES.NET
 .NET FRAMEWORK : VERSION 2.0
 LANGUAGE : C#.NET
Modules:
 New card
 Login
 Security information
 Transaction
 Verification
 Modules Description:
New card:
 In this module, the customer gives there information to enroll a new card.
 The information is all about there contact details. They can create there own login and password for there future use of the card.
Login
 In Login Form module presents site visitors with a form with username and password fields. If the user enters a valid username/password combination they will be granted access to additional resources on website. Which additional resources they will have access to can be configured separately.
Security information
 In Security information module it will get the information detail and its store’s in database.
 It has a set of question where the user has to answer the correctly to move to the transaction section.
Transaction
 The credit card owner initiates a credit card transaction by communicating to a credit card number that is stored in database.
 The information is accepted as "network data" in the data base only if a correct personal identification code (PIC) is used with the communication.
 The "network data" will serve to later authorize that specific transaction.
Verification
 In verification the process will seeks card number and if the card number is correct the relevant process will be executed.
 If the number is wrong, mail will be sent to the user saying the card no has been block and he can’t do the further transaction.
 Database design
SYSTEM IMPLEMENTATION
Implementation is the most crucial stage in achieving a successful system and giving the user’s confidence that the new system is workable and effective.
Implementation of a modified application to replace an existing one.
This type of conversation is relatively easy to handle, provide there are no major changes in the system.
02-04-2011, 11:47 AM
Post: #20
RE: Credit Card Fraud Detection Using Hidden Markov Models
Credit Card Fraud Detection Using Hidden Markov Model
Abstract

Now a day the usage 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.
EXISTING SYSTEM:
In case of the existing system each and every system are considered as a trusted computer. And so the attacker finds it easy to attack the system with fake signals. And also in the emerging network where many are used for some good propos. And in those there a lot of chance for the attacker to send unwanted information. In case of the fire alarm, if all the system are considered as trusted they could send false alarm where it lead to a heavy loss. And so we need a system to protect it. Hence we develop a new system.
PROPOSED SYSTEM:
The proposed system we introduce a new technology to protect the network. This is achieved by the following way. Realizing widespread adoption of such applications
Mandates sufficiently trustworthy computers that can be realized at low cost. Apart from facilitating deployment of futuristic applications, the ability to realize trustworthy computers at low cost can also addresses many of the security issues that plague our existing network infrastructure. Although, at first sight, “inexpensive” and “trustworthy”
May seem mutually exclusive, a possible strategy is to reduce the complexity of the components inside the trusted boundary. The often heard statement that “complexity is the enemy of security” is far from dogmatic. For one, lower complexity implies better verifiability of compliance. Furthermore, keeping the complexity inside the trust boundary at low levels can obviate the need for proactive measures for heat dissipation. Strategies constrained to simultaneously facilitate shielding and heat dissipation tend to be expensive. On the other hand, unconstrained shielding strategies can be reliable and inexpensive to facilitate.
HARDWARE CONFIGURATION
The hardware used for the development of the project is:
PROCESSOR : PENTIUM III 766 MHz
RAM : 128 MD SD RAM
MONITOR : 15” COLOR
HARD DISK : 20 GB
FLOPPY DRIVE : 1.44 MB
CDDRIVE : LG 52X
KEYBOARD : STANDARD 102 KEYS
MOUSE : 3 BUTTONS
SOFTWARE CONFIGURATION
The software used for the development of the project is:
OPERATING SYSTEM : Windows 2000 Professional
ENVIRONMENT : Visual Studio .NET 2005
.NET FRAMEWORK : Version 2.0
LANGUAGE : VB.NET
WEB TECHNOLOGY : Active Server Pages.NET
WEB SERVER : Internet Information Server 5.0
BACK END : SQL SERVER 2000
REPORTS : Web Form Data Grid control
27-07-2011, 10:49 AM
Post: #21
RE: Credit Card Fraud Detection Using Hidden Markov Models
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://www.google.co.in/url?sa=t&source=...sSa69yA60g
09-12-2011, 10:37 AM
Post: #22
RE: Credit Card Fraud Detection Using Hidden Markov Models
pls send the source code of dis project "credit card fraud detection using hidden markov model" to the mail address metsminiproject[at]gmail.com


thank u
11-01-2012, 01:23 PM
Post: #23
RE: Credit Card Fraud Detection Using Hidden Markov Models
hi i am doing M.Tech....i am trying to implement this paper.i am doing it in java but calculating state transition probability matrix(aij) is difficult for me. plz help me in solving it...
09-02-2012, 11:21 AM
Post: #24
RE: Credit Card Fraud Detection Using Hidden Markov Models
to get information about the topic Analysis on Credit Card Fraud Detection Methods full report ,ppt and related topic refer the link bellow
http://www.seminarprojects.com/Thread-cr...664?page=2

http://www.seminarprojects.com/Thread-cr...664?page=7

http://www.seminarprojects.com/Thread-di...-detection

http://www.seminarprojects.com/Thread-cr...dels--5664

http://www.seminarprojects.com/Thread-cr...del--13583

http://www.seminarprojects.com/Thread-cr...?pid=28600
06-03-2012, 12:30 AM
Post: #25
RE: Credit Card Fraud Detection Using Hidden Markov Models
Hello Everyone !!

I'm Implementing HMM As My Final Year project.

Can Anyone give me The "Source Code" in Java! I Dont Know .Net

Please that Would Be Great Helpful For My project.

Thanks.
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