An efficient credit card fraud detection approach using cost‐sensitive weak learner with imbalanced dataset

Abstract

The usage of credit cards is increasing in the digitization era to purchase goods online or offline, and the number of fraudulent credit card transactions has also increased. Fraudulent transaction detection is a big issue in the credit card domain because the data set is highly imbalanced. The performance of traditional classification techniques has decreased due to this issue. Therefore, a dynamic fraud detection system is required to overcome this issue. This article proposes an efficient cost-sensitive weak learner approach with a bagging and random forest classifier (CSWLB) to minimize misclas-