FedQ-Fraud: A Quantum-Enforced Federated Learning Framework for Financial Fraud Detection

What is it about?

Online financial fraud is a massive problem, with global losses reaching nearly half a trillion dollars. While Artificial Intelligence (AI) is a powerful tool for catching fraud, traditional methods often require banks to collect and share vast amounts of sensitive user data, creating major privacy risks. Our paper introduces a new system called FedQ-Fraud. This framework is designed to solve two problems at once: detect fraud accurately and protect user privacy. It works by combining three key technologies: 1. Deep Learning (DL): A smart AI model (a GRU) that learns to spot complex fraud patterns. 2. Federated Learning (FL): This is the privacy part. Instead of moving all the data to one place, the AI model is sent out to train on local data (e.g., at different banks). This means the private transaction data never leaves its secure source. We used a specific algorithm (MOON) to make sure this works well, even when the data at each bank is different. 3. Quantum Encryption (QEC): This is the security part. When the local models send their updates back to the central server, that communication could be a weak link. We use Quantum Key Distribution (QKD) to create "theoretically unbreakable" secret keys. This ensures the model updates are protected from eavesdroppers. Our combined system achieved 97.47% accuracy in detecting fraud, outperforming other common models. It proves that we can build highly effective, collaborative fraud detection systems without sacrificing user privacy or communication security.

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Photo by Julio Lopez on Unsplash

Photo by Julio Lopez on Unsplash

Why is it important?

This research provides a scalable, real-world solution that addresses the critical tug-of-war between security and privacy in finance. Currently, banks and financial institutions often have to choose: either improve fraud detection by using more data (risking privacy) or protect privacy (limiting the AI's effectiveness). Our FedQ-Fraud framework shows that you don't have to choose. It provides a clear path for institutions to collaborate on building powerful AI models that stop fraud, all while guaranteeing that their users' data stays private and that the communication itself is secured with quantum-level encryption. This work helps build the next generation of financial cybersecurity, one that is accurate, private, and secure by design.

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