The significant increase in credit card transactions can be attributed to the rapid growth of online shopping and digital payments, particularly during the COVID-19 pandemic. To safeguard cardholders, e-commerce companies, and financial institutions, the implementation of an effective and real-time fraud detection method using modern artificial intelligence techniques is imperative. However, the development of machine-learning-based approaches for fraud detection faces challenges such as inadequate transaction representation, noise labels, and data imbalance. Additionally, practical considerations like dynamic thresholds, concept drift, and verification latency need to be appropriately addressed. In this study, we designed a fraud detection method that accurately extracts a series of spatial and temporal representative features to precisely describe credit card transactions. Furthermore, several auxiliary self-supervised objectives were developed to model cardholders’ behavior sequences. By employing intelligent sampling strategies, potential noise labels were eliminated, thereby reducing the level of data imbalance. The developed method encompasses various innovative functions that cater to practical usage requirements. We applied this method to two real-world datasets, and the results indicated a higher F1 score compared to the most commonly used online fraud detection methods.

信用卡交易的大幅增长可归因于在线购物和数字支付的快速增长，特别是在 COVID-19 大流行期间。为了保护持卡人、电子商务公司和金融机构的利益，利用现代人工智能技术实施有效、实时的欺诈检测方法势在必行。然而，基于机器学习的欺诈检测方法的开发面临着交易表示不足、噪声标签和数据不平衡等挑战。此外，还需要适当解决动态阈值、概念漂移和验证延迟等实际考虑因素。在本研究中，我们设计了一种欺诈检测方法，可以准确地提取一系列空间和时间的代表性特征来精确描述信用卡交易。此外，还开发了几个辅助自我监督目标来模拟持卡人的行为序列。通过采用智能采样策略，消除了潜在的噪声标签，从而降低了数据不平衡的程度。所开发的方法包含满足实际使用需求的各种创新功能。我们将此方法应用于两个真实世界的数据集，结果表明与最常用的在线欺诈检测方法相比，F1 分数更高。