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08-02-2012, 05:58 PM
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09-02-2012, 11:21 AM
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18-03-2012, 03:39 AM
send ppt on credit card fraud detection methods...
I am studying in Information technology.so i wan ppt oon credit card fraud detection methods..
I am studying in Information technology.so i wan ppt oon credit card fraud detection methods..
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18-03-2012, 03:44 AM
i wan ppt on credit card fraud dection methods..so pls send
19-03-2012, 12:40 PM
to get information about the topic Analysis on Credit Card Fraud Detection Methods full report ,ppt and related topic refer the link bellow
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26-06-2013, 04:55 PM
Analysis on Credit Card Fraud Detection Methods
[attachment=56080]
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.
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.
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].
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.
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.
[attachment=56080]
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.
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.
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].
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.
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.