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Securing Financial Transactions: Exploring the Role of Federated Learning and Blockchain in Credit Card Fraud Detection
  • Pushpita Chatterjee ,
  • Debashis Das ,
  • Danda Rawat
Pushpita Chatterjee
Howard University

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Debashis Das
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Danda Rawat
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Abstract

Credit card fraud detection is a significant challenge for the financial industry, and the privacy of sensitive financial data is of utmost importance. Federated learning is a decentralized machine learning technique that can enable collaborative model training while preserving privacy. Blockchain, with its decentralized and secure nature, can further enhance the privacy and security of federated learning. This paper explores the opportunities, challenges, and future directions of blockchain-enabled federated learning for credit card fraud detection. The combination of federated learning and blockchain can provide a secure and private platform for credit card fraud detection. Blockchain-enabled federated learning offers several opportunities, including improved privacy, security, and collaboration among different financial institutions. The successful implementation of blockchain-enabled federated learning can revolutionize credit card fraud detection by providing a secure and private platform for collaborative model training. This paper emphasizes the potential of blockchain-enabled federated learning for credit card fraud detection and highlights the need to address the challenges associated with this technology. It is essential to continue exploring and developing blockchain-enabled federated learning to ensure the security and privacy of sensitive financial data while promoting collaboration and innovation in the financial industry.