Explainable Graph AI for Financial Market Risk Forecasting

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.