ABSTRACT:
Credit card fraud is a serious
problem in financial services. Billions of dollars are lost due to credit card
fraud every year. There is a lack of research studies on analyzing real-world
credit card data owing to confidentiality issues. In this paper, machine
learning algorithms are used to detect credit card fraud. Standard models are
first used. Then, hybrid methods which use AdaBoost
and majority voting methods are applied. To evaluate the model efficacy, a
publicly available credit card data set is used. Then, a real-world credit card
data set from a financial institution is analyzed. In addition, noise is added
to the data samples to further assess the robustness of the algorithms. The
experimental results positively indicate that the majority voting method
achieves good accuracy rates in detecting fraud cases in credit cards.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
·
System : Pentium Dual Core.
·
Hard Disk : 120 GB.
·
Monitor : 15’’ LED
·
Input Devices : Keyboard, Mouse
·
Ram : 1 GB
SOFTWARE REQUIREMENTS:
·
Operating system : Windows 7.
·
Coding Language : JAVA/J2EE
·
Tool : Netbeans 7.2.1
·
Database : MYSQL
REFERENCE:
KULDEEP RANDHAWA 1,
CHU KIONG LOO 1, (Senior Member, IEEE), MANJEEVAN SEERA 2,3, (Senior Member,
IEEE), CHEE PENG LIM4, AND ASOKE K. NANDI, “Credit Card Fraud Detection Using AdaBoost and Majority Voting”, IEEE Access, 2018.