IBRAHIM, Ishaku
Faculty Of Computing, Department Of Computer Science, Modibbo Adama University, Yola
A Review Of Deep Learning Approaches To Real-time Financial Fraud Detection, Research Gaps And The Path Forward
Financial fraud costs the global economy over $40 billion annually, yet existing detection systems continue to struggle at the intersection of accuracy, latency, interpretability, and fairness. This paper reviews the evolution of fraud detection methods from early rule-based systems and classical machine learning through recurrent and convolutional deep learning architectures, to transformer models, graph neural networks, and federated learning with

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