Adaptive Credit Risk Prediction Under Concept Drift Using Graph Neural Networks and Gradient Boosting
The proposed hybrid model combining a GraphSAGE encoder and XGBoost achieves an AUC of 0.852, outperforming standalone boosted trees on underwriting features (0.798) and a graph encoder alone (0.768).