EXPLOITING THE MACHINE-LEARNING TECHNIQUES FOR INTENSIFYING THE CREDIT CARD FRAUD DETECTION
Author(s):
Dr.M.CHINNARAO, M.SRAVYA, M.HARSHA VARDHAN, P.CHETANA, K.INDU PRIYA
Keywords:
credit card fraud detection, random forest algorithm, fraud detection, visualization, Decision tree.
Abstract
In this paper, mainly focused on credit card fraud detection for in real world. Initially collect the credit card datasets for trained dataset. Billions of dollars of loss are caused every year by fraudulent credit card transactions[1]. The design of efficient fraud detection algorithms is the key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data[2.3], the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. Then will provide the user credit card queries for testing data set. After classification process of random forest algorithm using to the already analyzing data set and user provide current dataset[4.5]. Finally optimizing the accuracy of the result data. Then will apply the processing of some of the attributes provided can find affected fraud detection in viewing the graphical model visualization. The performance of the techniques is evaluated based on accuracy, sensitivity, and specificity, precision[6]. The results indicate about the optimal accuracy for Decision tree are 98.6% respectively.
Article Details
Unique Paper ID: 159089

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 328 - 332
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