Graph-Based and Time-Series Hybrid Modeling for Fraud Detection
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.