A Review on Novel Approach to Handle Imbalanced Credit Card Transactions

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2018 by IJETT Journal
Volume-62 Number-2
Year of Publication : 2018
Authors : Sudhansu R. Lenka, Bikram K. Ratha, Biswaranjan Nayak
DOI :  10.14445/22315381/IJETT-V62P214


MLA Style: Sudhansu R. Lenka, Bikram K. Ratha, Biswaranjan Nayak "A Review on Novel Approach to Handle Imbalanced Credit Card Transactions" International Journal of Engineering Trends and Technology 62.2 (2018): 80-95.

APA Style:Sudhansu R. Lenka, Bikram K. Ratha, Biswaranjan Nayak (2018). A Review on Novel Approach to Handle Imbalanced Credit Card Transactions International Journal of Engineering Trends and Technology, 62(2), 80-95.

Recently, most of the people are using credit and debit cards for every purchase and payment. So the excessive usage of these cards attracts the illicit to implement different techniques to create fraudulent activities against these transactions. As a result, each year billions of dollars are lost due to ineffectiveness of the fraud detection system. Credit card transactions are highly imbalanced, since most of them are genuine and very few are fraudulent. These imbalance transactions lead to a huge challenge for the machine learning and data mining algorithms. A single algorithm cannot accelerate the performance of the model, so ensemble of classifiers is the approach to handle such an issue. In this paper, we first provide different approaches to detect frauds, the methods to evaluate the performance and the challenges faced by the fraud detection model. Second, we present a comprehensive review on the imbalanced problem, state of the art on ensembles techniques, assessment measures to evaluate the algorithm performance and finally, we perform different comparison tests among the ensemble-based methods.The ensemble-based methods are classified into different categories to handle imbalanced fraudulent transactions where each method is grouped based on their working principle. The comparison tests of different methods have shown that the performance of the detection model can be improved by integrating random undersampling approaches with bagging or boosting methods. Additionally, the results justifies that ensemble-based methods are worthwhile in integrating the pre-processing techniques before learning the classifier.

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Bagging, Boosting, Cost-sensitive learning, Ensembles, Imbalance data set, Credit card fraud.