Knowledge Graph-Enhanced AI for Financial Risk Propagation Analysis
Keywords:
knowledge graphs, financial risk propagation, systemic risk, graph neural networks, AI governance, financial infrastructure, fairness, policyAbstract
Financial risk propagation is increasingly understood as a systemic phenomenon shaped by contractual exposures, market sentiment, operational dependencies, and cross-border institutional linkages. Conventional risk analytics often rely on siloed datasets, static correlation estimates, and periodic reporting, which limit their ability to trace contagion through heterogeneous financial networks. This paper examines knowledge graph-enhanced artificial intelligence as a system-level architecture for analyzing financial risk propagation. It argues that knowledge graphs provide a semantic and relational substrate capable of integrating entities, instruments, events, jurisdictions, and temporal states, while graph-based machine learning enables inference over latent transmission channels. The paper develops a layered architecture spanning data acquisition, ontology governance, graph construction, representation learning, multi-agent decision support, and human oversight. It evaluates structural trade-offs involving scalability, interpretability, privacy, latency, and resilience under market regime shifts. The analysis also addresses deployment sustainability, infrastructure dependencies, fairness in credit and supervisory contexts, and policy implications for regulators and financial institutions. Through cross-domain comparisons with supply chain, energy, and public health networks, the paper identifies reusable design principles and governance constraints. It concludes that knowledge graph-enhanced AI can improve risk visibility and adaptive decision-making, but only when coupled with robust data stewardship, explainability mechanisms, distributed infrastructure, and accountable institutional governance.
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