Explainable Multi-Modal Graph Survival Learning for Systemic Credit Risk Propagation and Early Default Prediction in Dynamic Financial Networks
Keywords:
systemic credit risk, graph neural networks, survival analysis, explainable artificial intelligence, dynamic financial networks, early default prediction, macroprudential policyAbstract
Systemic credit risk propagates through dense, time-varying interconnections among financial institutions, making early default prediction and the identification of contagion pathways essential for financial stability. This paper presents a comprehensive systems-oriented analysis of an explainable multi-modal graph survival learning framework designed to model dynamic financial networks and forecast firm-level default probabilities. The architecture integrates heterogeneous data modalities, including market prices, balance-sheet fundamentals, textual disclosures, and network-derived topological signals, into a unified graph neural network backbone that respects the continuous-time-to-event nature of credit defaults. The discussion emphasizes structural trade-offs between predictive accuracy and interpretability, infrastructure requirements for real-time deployment, and the governance mechanisms necessary to ensure robustness, fairness, and regulatory acceptance. By situating the model within the broader landscape of macroprudential oversight, the paper examines how explainability techniques such as counterfactual path attribution and integrated gradient decomposition can deliver actionable risk attributions to supervisors. Furthermore, it addresses sustainability concerns by linking network resilience with environmental, social, and governance factors, and explores dynamic policy levers informed by model outputs. The analysis refrains from mathematical formalization and instead focuses on system-level design choices, deployment constraints, and the institutional frameworks that determine whether advanced graph survival learning can transition from academic prototypes to trusted components of systemic risk surveillance infrastructure.
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