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.

中文翻译：

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一种基于数据分布的异构集成学习模型，用于信用卡欺诈检测

信用卡欺诈检测 (CCFD) 对于保护持卡人的财产和银行的声誉非常重要。信用卡交易数据的类别不平衡是影响当前检测模型分类性能的主要因素。然而，先前的方法旨在提高少数类样本（欺诈交易）的预测准确性，但这通常会导致模型对多数类样本（合法交易）的预测性能显着下降，从而大大增加调查成本对于银行。在本文中，我们提出了一种基于数据分布（HELMDD）的异构集成学习模型来处理 CCFD 中的不平衡数据。我们在两个真实的信用卡数据集上验证了 HELMDD 的有效性。实验结果表明，与当前最先进的模型相比，HELMDD 具有最佳的综合性能。HELMDD 不仅对少数类和多数类都实现了良好的召回率，而且还将银行的储蓄率分别提高到 0.8623 和 0.6696。