ABSTRACT

Banks have used early fraud warning systems for some years. Improved fraud detection thus has become essential to maintain the viability of the payment system. Outlier mining in data mining is an important functionality of the existing algorithms which can be divided into methods based on statistical, distance based methods, density based methods and deviation based methods. In this article I propose the concept of credit card fraud detection by using a data stream outlier detection algorithm which is based on reverse k-nearest neighbors (SODRNN). The distinct quality of SODRNN algorithm is it needs only one pass of scan. Whereas traditional methods need to scan the database many times, it is not suitable for data stream environment.