Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning
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Abstract
Detecting financial fraud in real time is an ongoing challenge due to the ever-evolving nature of fraudulent activities. Conventional fraud detection systems rely heavily on static machine learning models, which often struggle to adapt to emerging fraud patterns. Additionally, data privacy regulations and institutional constraints limit collaborative fraud detection efforts, as financial organizations are often unable to share sensitive transactional data. In this research, we introduce a real-time fraud detection framework that combines Adaptive Graph Neural Networks (GNNs) and Federated Learning (FL) to overcome these limitations. The GNN component dynamically models relationships within financial transactions, allowing the system to detect suspicious patterns as they emerge rather than relying on historical fraud markers. Meanwhile, federated learning enables multiple financial institutions to collaboratively train fraud detection models without directly sharing customer data, thus addressing privacy concerns. To enhance explainability and regulatory compliance, the proposed system integrates Explainable AI (XAI) methods, making fraud detection decisions more transparent. Experimental evaluations on benchmark financial datasets and real-world transactional data reveal that our approach improves fraud detection accuracy by 15–30% while reducing false positives compared to existing machine learning-based solutions. The findings highlight the potential of GNNs and FL in advancing fraud prevention strategies while maintaining data security and interpretability, making it a promising alternative to traditional fraud detection mechanisms.
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