Graph-Enhanced Fraud Detection: A Hybrid Graph Neural Network and XGBoost Framework for Credit Card Transaction Classification

Credit card fraud detection remains one of the most pressing challenges in financial technology, costing institutions an estimated $32 billion annually.This paper proposes GNN-XGB, a hybrid framework combining Graph Neural Networks with XGBoost to exploit individual transaction features andgraph-neighborhood patterns. We construct a transaction graph of 10,000 nodes and 629,820 edges, apply two-layer mean-aggregation message passing,and feed graph features into XGBoost. Our framework achieves F1 Score of 96.99% compared to 49.15% baseline, a +47.84% improvement. Graph featuresaccount for over 90% of predictive power, confirming fraud is a network phenomenon. Implementation uses pure Python with no specialized libraries.