Explainable Graph AI for Financial Market Risk Forecasting

  • Authors

    • Alexey Lyapunov Professor, Moscow State University, Russia. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2025PII3L9S

    Published 12-05-2025

  • Explainable Artificial Intelligence (Xai), Graph Neural Networks (Gnn), Graph Attention Networks (Gat), Financial Market Risk, Financial Analytics, Deep Learning, Risk Forecasting, Explainable Graph Ai, Financial Networks, Intelligent Decision Support

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    A. Lyapunov, “Explainable Graph AI for Financial Market Risk Forecasting”, IJETMR, vol. 8, no. 2, pp. 01–18, Dec. 2025, doi: 10.67228/30715636/IJETMR-2025PII3L9S.

  • Abstract

    The proposed Explainable Graph Artificial Intelligence Framework for Financial Risk Forecasting (XGraph-FR) integrates Graph Neural Networks (GNNs) and Explainable AI (XAI) to deliver accurate and interpretable financial risk predictions. It models financial markets as a heterogeneous graph connecting companies, sectors, investors, and macroeconomic indicators through weighted relationships. Graph Attention Networks (GATs) capture dynamic dependencies using data from stock prices, financial statements, macroeconomic indicators, news, and social media sentiment. The framework employs preprocessing techniques such as normalization, missing-value imputation, graph construction, and temporal segmentation to improve data quality. Explainability is provided through attention visualization, feature attribution, graph saliency, and subgraph extraction, allowing stakeholders to understand prediction outcomes. Performance is evaluated using metrics including accuracy, precision, recall, F1-score, AUC, explainability, and computational efficiency, supporting transparent and reliable financial risk forecasting for portfolio management, fraud detection, market surveillance, and regulatory compliance.

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