---
title: Dynamic Graph Neural Networks for Real Time Systemic Risk Surveillance: An Explainable AI Framework for Financial Stability
url: https://www.ml-quant.com/papers/ssrn/7522878/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-02
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 7522878
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7522878
featured: 2026-10-02
citations: unknown
topic: ML & AI Methods
---


# Dynamic Graph Neural Networks for Real Time Systemic Risk Surveillance: An Explainable AI Framework for Financial Stability

The research proposes a temporal graph neural network with explainability tools for real-time systemic risk surveillance, achieving early warning signals 3-4 quarters ahead of financial distress on bank data.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7522878
- Identifier: SSRN 7522878
- Released: 2026-09-29
- First featured: Quant Letter No. 133 (2026-10-02): https://www.ml-quant.com/issues/2026-10-02/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods
- Authors: Gnana Praveena Nethala, Tirumala Rao Bellamkonda, Rajyalakshmi Chavakula, Ravindar Tadi, Ram Soorat

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