ST-GAT applies spatial-temporal graph attention networks to reconstructed interbank graphs from FDIC Call Reports, achieving 0.939 AUPRC for bank distress prediction with explainable feature importance.
The framework's explainability contribution rests on two validated layers: temporal attention attribution and permutation-based feature importance
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Explainable Graph Neural Networks for Interbank Contagion Surveillance: A Regulatory-Aligned Framework for the U.S. Banking Sector
ST-GAT applies spatial-temporal graph attention networks to reconstructed interbank graphs from FDIC Call Reports, achieving 0.939 AUPRC for bank distress prediction with explainable feature importance.