Outlier Detection Using Oversampling PCA for Credit Card Fraud Detection
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 5
Abstract
Credit card fraud detection is an important application of outlier detection in recent years. Many outlier detection techniques are available but they are working in batch mode, due to this those techniques are not applicable for applications where large amount of data is present. In this paper, a new credit card fraud detection method based on Oversampling Principal Component Analysis (osPCA) with low computation and memory requirements is presented. Principal Component Analysis (PCA) is a tool in data analysis for dimension reduction, it transforms high dimensional data into lower dimensions which contains maximum amount of information. Here we are using concept of oversampling on data and then input is given to PCA, therefore it is possible to detect credit card frauds from large amount of data. This technique is suitable for online applications which have memory or computation limitation.
Authors and Affiliations
Amruta D. Pawar, Seema A. Dongare, Amol L. Deokate, Harshal S. Sangle, Panchsheela V. Mokal
Keywords
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