Credit Card Fraud Detection Using Random Forest Algorithm

Abstract

Our project mainly focuses on credit card fraud detection in real world. In this proposed project
we designed a protocol or a model to detect the fraud activity in credit card transactions. This
system is capable of providing most of the essential features required to detect fraudulent and
legitimate transactions. As technology changes, it becomes difficult to track the behavior and
pattern of fraudulent transactions. With the rise of machine learning, artificial intelligence and
other relevant fields of information technology, it becomes feasible to automate this process and
to save some of the intensive amount of labor that is put into detecting credit card fraud. Initially
we will collect the credit card datasets for trained dataset. Then we will provide the user credit
card queries for testing data set. After classification process of dataset random forest algorithm is
used for analyzing data set and current dataset provided by the user. After final optimization the
results indicates about the optimal accuracy for Random Forest Algorithm which is 98.6% of the
accuracy of the result data.