Digital twin for credit card fraud detection: opportunities, challenges, and fraud detection advancements

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.