Integrating Graph Neural Networks and Transformer Models for Financial Risk Assessment in Dynamic Markets

Abstract

The increasing complexity and interconnectedness of modern financial systems pose significant challenges to traditional risk assessment models. Conventional statistical and machine learning approaches often fail to capture non-linear dependencies and dynamic temporal patterns among financial entities. This paper proposes a hybrid deep learning framework that integrates Graph Neural Networks (GNNs) and Transformer architectures to model both structural and temporal correlations in financial markets. The GNN component encodes inter-firm and cross-sector relationships using graph embeddings, while the Transformer component captures evolving sequential dependencies from time-series data such as asset prices, credit ratings, and macroeconomic indicators. The integrated architecture leverages multi-head attention and message-passing mechanisms to jointly learn spatial and temporal dependencies, producing a comprehensive representation of financial risks. Experiments conducted on multiple real-world financial datasets, including equity market indices and corporate bond spreads, demonstrate the model’s superior performance in predicting credit risk and market volatility compared to benchmark methods. The results show a notable improvement in accuracy, stability, and interpretability, indicating that the proposed hybrid framework provides a powerful and explainable approach for dynamic financial risk modeling.