{
  "id": 5857,
  "url": "https://arxiv.org/abs/2604.14232v1",
  "title": "Explainable Graph Neural Networks for Interbank Contagion Surveillance: A Regulatory-Aligned Framework for the U.S. Banking Sector",
  "summary": "The Spatial-Temporal Graph Attention Network (ST-GAT) framework was created to serve as an explainable GNN-based solution for detecting bank distress early warning signs and for conducting macro-prudential surveillance of the interbank system in the United States. The ST-GAT framework models 8,103 FDIC insured institutions across 58 quarterly snapshots (2010Q1-2024Q2). Bilateral exposures were reconstructed from publicly available FDIC Call Reports using maximum entropy estimation to produce a d",
  "authors": "Mohammad Nasir Uddin",
  "category": "research",
  "topics": "regulation,privacy-surveillance,transparency,finance-investment",
  "orgs": null,
  "regions": "us",
  "published_at": "2026-04-14T19:07:28.000Z",
  "fetched_at": "2026-07-14T16:32:02.062Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5857",
  "original_url": "https://arxiv.org/abs/2604.14232v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}