21-05-2012, 01:08 PM
Analysis of Credit Card Fraud Detection Methods
[attachment=22559]
INTRODUCTION
The use of credit cards is prevalent in modern day
society. Detecting credit card fraud is a difficult task
when using normal procedures, so the development of the
credit card fraud detection model has become of
significance, whether in the academic or business
community recently.
In this paper, three approaches to fraud detection are
presented. The clustering model, the probability density
estimation method and the model based on Bayesian
networks. This paper investigates the usefulness of
applying different approaches to a problem of Credit card
fraud detection.
RELATEDWORK ON CREDITCARD FRAUD DETECTION
From the work of view for preventing credit card
fraud, more research works were carried out with special
emphasis on data mining and neural networks. Sam and
Karl [1] suggest a credit card fraud detection system
using Bayesian and neural network techniques to learn
models of fraudulent credit card transactions. Kim and
Kim have identified skewed distribution of data and mix
of Legitimate and fraudulent transactions as the two main
reasons for the complexity of credit card fraud detection
[2].This paper investigates the usefulness of applying
different learning approaches.
BAYESIAN NETWORKS
For the purpose of fraud detection, two Bayesian
networks to describe the behavior of user are constructed.
First, a Bayesian network is constructed to model
behavior under the assumption that the user is fraudulent
(F) and another model under the assumption the user is a
legitimate (NF). The ‘fraud net’ is set up by using expert
knowledge. The ‘user net’ is set up by using data from
non fraudulent users.
CONCLUSION AND FUTURE WORK
Credit card fraud has become more and more rampant
in recent years. To improve merchants’ risk management
level in an automatic and effective way, building an
accurate and easy handling credit card risk monitoring
system is one of the key tasks for the merchant banks.
One aim of this study is to identify the user model that
best identifies fraud cases. The models are compared in
terms of their performances. To improve the fraud
detection system, the combination of the three presented
methods could be beneficial. It is possible to use
Bayesian Networks based on the input representation
method and the developed clustering model in the real
fraud detection system. In the future, these models can
extend to use in health insurance fraud detection.
[attachment=22559]
INTRODUCTION
The use of credit cards is prevalent in modern day
society. Detecting credit card fraud is a difficult task
when using normal procedures, so the development of the
credit card fraud detection model has become of
significance, whether in the academic or business
community recently.
In this paper, three approaches to fraud detection are
presented. The clustering model, the probability density
estimation method and the model based on Bayesian
networks. This paper investigates the usefulness of
applying different approaches to a problem of Credit card
fraud detection.
RELATEDWORK ON CREDITCARD FRAUD DETECTION
From the work of view for preventing credit card
fraud, more research works were carried out with special
emphasis on data mining and neural networks. Sam and
Karl [1] suggest a credit card fraud detection system
using Bayesian and neural network techniques to learn
models of fraudulent credit card transactions. Kim and
Kim have identified skewed distribution of data and mix
of Legitimate and fraudulent transactions as the two main
reasons for the complexity of credit card fraud detection
[2].This paper investigates the usefulness of applying
different learning approaches.
BAYESIAN NETWORKS
For the purpose of fraud detection, two Bayesian
networks to describe the behavior of user are constructed.
First, a Bayesian network is constructed to model
behavior under the assumption that the user is fraudulent
(F) and another model under the assumption the user is a
legitimate (NF). The ‘fraud net’ is set up by using expert
knowledge. The ‘user net’ is set up by using data from
non fraudulent users.
CONCLUSION AND FUTURE WORK
Credit card fraud has become more and more rampant
in recent years. To improve merchants’ risk management
level in an automatic and effective way, building an
accurate and easy handling credit card risk monitoring
system is one of the key tasks for the merchant banks.
One aim of this study is to identify the user model that
best identifies fraud cases. The models are compared in
terms of their performances. To improve the fraud
detection system, the combination of the three presented
methods could be beneficial. It is possible to use
Bayesian Networks based on the input representation
method and the developed clustering model in the real
fraud detection system. In the future, these models can
extend to use in health insurance fraud detection.