CREDIT CARD FRAUD DETECTION SYSTEM USING MACHINE LEARNING

Abstract

In recent years credit card became one of the essential parts of the people. Sudden increase in E
commerce, customer started using credit card for online purchasing therefore risk of fraud also 
increases. Instead of carrying a huge amount in hand it is easier to keep credit cards. But nowadays 
that too becomes unsafe. Now a days we are facing a big problem on credit card fraud which is 
increasing in a good percentage. The main purpose is the survey on the various methods applied to detect 
credit card frauds. From the abnormalities, in the transaction, the fraudulent one is identified. We 
address this issue in order to implement some machine learning algorithm like random forest, logistic 
regression in order to detect this kind of fraud. In this paper we increase the efficiency in finding the 
fraud. However, we discussed and evaluated employee criteria. Currently, the issues of credit card 
fraud detection have become a big problem for new researchers. We implement an intelligent algorithm 
which will detect all kind of fraud in a credit card transaction. We handled the problem by finding a 
pattern of each customer in between fraud and legal transaction. Isolation Forest Algorithm and Local 
Outlier Factor are used to predict the pattern of transaction for each customer and a decision is made 
according to them. In order to prevent data from mismatching, all attribute are marked equally.