This project develops a hybrid fraud detection model by combining graph-based learning and time-series analysis to capture complex transaction relationships, aiming to enhance fraud prediction accuracy while minimizing false positives in large-scale payment datasets.
Comparing hybrid neural networks and traditional models for financial time series.
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