IMPROVING CREDIT CARD FRAUD DETECTION SYSTEM USING K-MEANS CLUSTERING ALGORITHM

Abstract

Now a day the usage of credit cards has been dramatically increased. 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 I propose a system for credit card fraud detection and tried to improve the performance of an existing system. In doing so, we did not undertake the typical objective of maximizing the number of correctly classified transactions but rather we defined a new objective function where the misclassification costs are variable and thus, correct classification of some transactions are more important than correctly classifying the others. This proposed model makes use of k-Means Clustering algorithms which is a novel one in the related literature, both in terms of the application domain and the cross-over operator used. The algorithm is applied to real life data where the savings obtained are almost three times the current practice. At the same time, we try to ensure that genuine transactions are not rejected. Here by I presented a detailed experimental result to show the effectiveness of our approach and compare it with other techniques available in the literature.