Ranking, Not Point Estimates: Risk Tiering and the Case for AI-Assisted Loss Data in Caribbean SIDS Credit

PURPOSE: To examine what physical risk information remains identifiable for credit in Caribbean Small Island Developing States (SIDS), where hazard data are rich but loss records scarce. DESIGN/METHODOLOGY/APPROACH: Using storm tracks and four damage-function calibrations, we assess Caribbean risk rankings through Monte Carlo simulation, stochastic dominance and independent records, and compare Caribbean and United States wind-loss relationships using wild cluster bootstrap inference. FINDINGS: Territory tiering is identifiable and broadly stable across damage-function choice, whereas point magnitudes remain imprecise under sparse Caribbean loss records. ORIGINALITY/VALUE: We identify an ordinal-cardinal boundary in Caribbean SIDS: risk tiering supports screening today, while precise loss estimates for pricing require better records that artificial intelligence (AI)-assisted methods may help develop. RESEARCH LIMITATIONS/IMPLICATIONS: Territory tiering can support risk screening, while data limitations motivate further research on AI-assisted loss-data generation for finer pricing. KEYWORDS: Credit Risk Management; SIDS; Climate Finance; Climate Risk; Data Scarcity; Economic Losses; Caribbean. CITATION: Jia, X. and Capodagli, S. (2026): Ranking, Not Point Estimates: Risk Tiering and the Case for AI-Assisted Loss Data in Caribbean SIDS Credit. World Journal of Entrepreneurship, Management and Sustainable Development (WJEMSD), Vol. 22, No. 6, pp. 585-600.