Artificial Intelligence in Disaster Supply Chain Risk Management: A Bibliometric Analysis with Financial Risk Implications

Disruptions caused by disasters, pandemics, and systemic crises have increased the complexity and vulnerability of global supply chains, highlighting the need for advanced analytical approaches to risk and resilience management. In this context, artificial intelligence (AI) has emerged as a promising analytical capability for improving risk assessment and decision-making in disrupted supply chains. The study follows PRISMA 2020 reporting guidelines adapted for bibliometric research and presents a bibliometric and knowledge-mapping analysis of artificial intelligence applications in disaster supply chain risk and resilience management. Using the Web of Science Core Collection, a dataset of 288 peer-reviewed publications was analyzed through keyword co-occurrence, bibliographic coupling, citation analysis, and collaboration network mapping. The findings indicate a rapidly expanding research field in which AI supports predictive risk assessment, real-time monitoring, and resilience-oriented decision-making in disaster-prone supply networks. The analysis identifies dominant thematic clusters, emerging research directions, and opportunities for integrating AI-enabled analytics into supply chain risk management frameworks. The mapped literature also suggests secondary interpretive implications for financial risk exposure and supply chain finance, rather than indicating a separately operationalized finance-specific bibliometric subfield. To enhance interpretive depth, an AI-assisted analytical layer was applied to refine thematic clusters and detect emerging trends. However, this layer operates as a complementary interpretive tool and is subject to methodological limitations, including sensitivity to keyword semantics, dependence on bibliometric outputs, and potential interpretive bias in AI-assisted thematic labeling. Consequently, the AI-assisted analysis is used to support, rather than replace, bibliometric findings. Overall, this study contributes to the emerging literature on artificial intelligence in disaster supply chain risk management and highlights future research opportunities, including improved methodological integration and enhanced analytical transparency in AI-assisted bibliometric research.