Fiore, U., De Santis, A., Perla, F., Zanetti, P. and Palmieri, F. (2019) Using Generative Adversarial Net-Works for Improving Classification Effectiveness in Credit Card Fraud Detection. Information Sciences, 479, 448-455.
https://doi.org/10.1016/j.ins.2017.12.030

has been cited by the following article:

• TITLE: Credit Card Fraud Detection Using Weighted Support Vector Machine

  AUTHORS: Dongfang Zhang, Basu Bhandari, Dennis Black

  KEYWORDS: Support Vector Machine, Binary Classification, Imbalanced Data, Undersampling, Credit Card Fraud

  JOURNAL NAME: Applied Mathematics, Vol.11 No.12, December 21, 2020

  ABSTRACT: Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.