FINNET: A Hierarchical Graph Learning Framework for Adaptive Cross-Market Financial Risk Prediction

Title

FINNET: A Hierarchical Graph Learning Framework for Adaptive Cross-Market Financial Risk Prediction

Creator

Anandu, P.S.; Gogi, Vyshali J.; Azarudheen, S.

Description

Systemic financial risk emerges from complex multi-scale interactions among entities, sectors, and markets. We introduce FINNET, a hierarchical graph neural network framework that models these vertical dependencies through volatility-aware adaptive pooling. Our approach features: (1) a tri-scale graph structure capturing entity, sector, and market dynamics; (2) dynamic embeddings combining static features with time-varying signals; (3) transfer learning for emerging markets; and (4) transparent risk decomposition for regulatory compliance. Validated on 58,432 financial entities across three continents, FINNET achieves 0.891 AUC with only 3.8% performance degradation during crises, while providing early warnings 15 days before failures. 2026 IEEE.

Source

Proceedings of the 4th IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2026;

Date

01-01-2026

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Dynamic Embeddings; Explanatory AI; Graph Neural Networks in Finance; Multi-Scale Learning; Systemic Risk; Transfer Learning

Coverage

Anandu P.S., Christ (Deemed to be University), Department of Data Science, Bangalore Central Campus, India; Gogi V.J., Christ (Deemed to be University), Department of Data Science, Bangalore Central Campus, India; Azarudheen S., Christ (Deemed to be University), Department of Data Science, Bangalore Central Campus, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833154970-1;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/IATMSI68868.2026.11466022

https://www.scopus.com/pages/publications/105036968997?origin=resultslist