<p>Credit risk assessment plays a critical role in financial decision-making by estimating the likelihood of loan default. While traditional models such as logistic regression, decision trees, and ensemble methods are widely used, they often fall short in capturing complex borrower relationships and dynamic financial behavior. Recent advances in graph-based learning have shown promise in modeling credit networks more effectively. In this study, we propose a Hybrid Multi-Stage Deep Learning model with Graph-Based Financial Network Representation (HMDL-GFNR) that integrates Graph Neural Networks (GNNs), Transformer-based temporal modeling, and a CATBoost classifier to improve credit risk prediction. The proposed architecture captures both relational borrower-lender structures and evolving credit patterns, addressing key limitations of existing methods. We evaluate HMDL-GFNR on benchmark financial datasets and compare its performance with state-of-the-art models including XGBoost, Random Forest, and recent graph-based approaches from contemporary literature. Our model consistently outperforms baselines across precision, recall, F1-score, and AUC-ROC, with a notable reduction in false negatives–crucial for minimizing financial loss. Additionally, we incorporate SHAP-based interpretability and attention-based insights to ensure transparency and regulatory alignment. The findings affirm the relevance of hybrid graph-temporal modeling in credit scoring and provide a scalable, interpretable, and high-performing solution for real-world risk assessment.</p>