Catastrophic “Hyperclustering” and Recurrent Losses: Diagnosing U.S. Flood Insurance Insolvency Triggers

Adam Nayak , Mengjie Zhang, Pierre Gentine , Upmanu Lall 

Although a cornerstone of U.S. flood risk preparedness since 1968, the National Flood Insurance Program (NFIP), is burdened by insolvency. Despite pricing and risk assessment reforms, systemic failures persist, resulting in accumulation of billions in federal debt. This study presents an interdisciplinary framework integrating qualitative synthesis, unsupervised machine learning, and game theory to diagnose triggers of insolvency. We identify catastrophic “hyperclustering" as large-scale flood events spanning days to weeks and induced by a common hydrometeorological driver, which dominate claim volumes often in regions of high asset density. We find chronic annual losses arise from recurrent claims, emphasizing the need for proactive managed retreat from high-risk areas. Our findings support targeted NFIP reform and broader risk management, particularly as climate extremes intensify the homeowners’ insurance crisis. We argue that long-term resilience requires aligning financial, structural, and non-structural interventions with distinct regional risk patterns—whether driven by hyperclustering, recurrent losses, or both.