Credit cards are widely used for payments due to their convenience and broad acceptance. Their popularity comes with the critical challenge of safeguarding personal and payment information from fraud and unauthorized access. Robust security measures are crucial to maintaining trust and confidence among users. In response to this pressing issue, this paper focuses on credit card fraud detection, its challenges, and innovative solutions using digital twins and blockchain. This research highlights the importance of understanding and reducing credit card fraud to protect consumers and financial institutions. The study provides a detailed overview of credit card fraud analysis and categorizes its different types to clarify the threat landscape. It introduces a new digital twin approach to improve fraud detection. Digital twins are virtual replicas of physical systems that show promise for enhancing anomaly detection and behavioral analysis for more precise and timely fraud identification. In addition, the paper examines blockchain-enabled federated learning (BFL) as a decentralized method that uses blockchain’s security features to improve collaborative learning. By merging digital twins with federated learning (FL), the study presents a dynamic strategy for identifying known and emerging fraud patterns effectively. These advanced technologies represent a significant step forward in combating credit card fraud. Overall, the research not only focuses on creating more robust fraud detection systems but also emphasizes the importance of continuous innovation and adaptation to enhance financial security measures.

中文翻译：

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用于信用卡欺诈检测的数字孪生：机遇、挑战和欺诈检测进展


信用卡因其便利性和广泛接受性而被广泛用于支付。它们的流行伴随着保护个人和支付信息免受欺诈和未经授权的访问的严峻挑战。强大的安全措施对于维持用户之间的信任和信心至关重要。为了应对这一紧迫问题，本文重点讨论信用卡欺诈检测、其挑战以及使用数字孪生和区块链的创新解决方案。这项研究强调了了解和减少信用卡欺诈对于保护消费者和金融机构的重要性。该研究详细概述了信用卡欺诈分析，并对其不同类型进行了分类，以阐明威胁情况。它引入了一种新的数字孪生方法来改进欺诈检测。数字孪生是物理系统的虚拟复制品，有望增强异常检测和行为分析，从而更准确、更及时地识别欺诈。此外，本文还研究了基于区块链的联邦学习（BFL）作为一种去中心化方法，利用区块链的安全功能来改善协作学习。通过将数字孪生与联邦学习 (FL) 相结合，该研究提出了一种有效识别已知和新出现的欺诈模式的动态策略。这些先进技术代表着在打击信用卡欺诈方面向前迈出了重要一步。总体而言，该研究不仅侧重于创建更强大的欺诈检测系统，还强调持续创新和适应以增强金融安全措施的重要性。