Demystifying Knowledge Graphs for AI-Enhanced Financial Decision Support with Graph Neural Networks

Abstract

This article explores how knowledge graphs and Graph Neural Networks (GNNs) transform financial decision support systems. Traditional AI approaches in finance struggle with the interconnected nature of financial ecosystems, where relationships between entities are as crucial as the entities themselves. Knowledge graphs address this limitation by creating semantic networks that capture complex financial relationships, while GNNs provide the architecture to learn from these structures effectively. Together, they enable contextual understanding of financial data, supporting enhanced risk assessment, fraud detection, personalized advice, and market intelligence. These technologies also significantly improve AI decision explainability—critical in regulated financial services. It  examines the components of financial knowledge graphs, GNN architectural design for financial applications, key use cases, explainability benefits, and adoption challenges. As financial institutions increasingly seek relationship-centered intelligence, these combined technologies represent a paradigm shift from isolated data analysis toward holistic understanding of financial systems.