Abstract
Credit card fraud detection (CCFD) is important for protecting the cardholder’s property and the reputation of banks. Class imbalance in credit card transaction data is a primary factor affecting the classification performance of current detection models. However, prior approaches are aimed at improving the prediction accuracy of the minority class samples (fraudulent transactions), but this usually leads to a significant drop in the model’s predictive performance for the majority class samples (legal transactions), which greatly increases the investigation cost for banks. In this paper, we propose a heterogeneous ensemble learning model based on data distribution (HELMDD) to deal with imbalanced data in CCFD. We validate the effectiveness of HELMDD on two real credit card datasets. The experimental results demonstrate that compared with current state‐of‐the‐art models, HELMDD has the best comprehensive performance. HELMDD not only achieves good recall rates for both the minority class and the majority class but also increases the savings rate for banks to 0.8623 and 0.6696, respectively.
Highlights
With the rapid development of mobile internet and ecommerce technologies, online payment tools such as credit cards are welcomed by more and more people
To address the above issues, we propose a new kind of heterogeneous ensemble learning model based on data distribution (HELMDD) for credit card fraud detection
We introduce the proposed heterogeneous ensemble learning model based on data distribution (HELMDD) in details, which consists of two main components
Summary
With the rapid development of mobile internet and ecommerce technologies, online payment tools such as credit cards are welcomed by more and more people. While credit cards bring convenience to customers, they expose cardholders and banks to potential fraud risks [1, 2]. Credit card fraud is a global problem. Fraud prevention and fraud detection are two main ways to combat credit card fraud [4]. Used technologies in fraud prevention include secure payment gateways, intrusion detection systems, and firewalls [5]. Fraud detection takes place after the fraud prevention mechanism has been breached [4], which means that fraud detection is the last line of defense to ensure the security of credit card transactions. Banks have to invest considerable money to optimize their fraud detection system [6], due to the need to protect cardholder’s funds and their own business reputation
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