Shimeng Huang
Purdue University
Room: M3 3127
Bridging the Disaster Protection Gap with Index Insurance
Abstract: Natural disasters have become increasingly frequent and severe, leading to rising financial costs. Yet, the insurance protection gap remains substantial, with over 60% of global economic losses uninsured between 2012 and 2021. Index insurance, a relatively new approach, offers a potential solution by providing payouts based on pre-specified indices, significantly reducing both costs and settlement times compared to traditional indemnity insurance. This raises the question: can index insurance complement indemnity insurance in bridging the disaster protection gap? While promising, index insurance faces the challenge of basis risk—the discrepancy between the index and the actual losses. This paper develops a conceptual framework comparing an indemnity-only market with a joint market offering both indemnity and index insurance, showing how index insurance complements indemnity coverage in bridging the protection gap. Our empirical analysis focuses on flood insurance. By leveraging rich, yet complex, weather data and advanced deep learning techniques, we develop a modeled index designed to forecast ultimate flood losses. Specifically, to capture the intricate effects of compound weather events, we propose a neural-network-based predictive model. This model features a recurrent neural network with an attention mechanism to capture the temporal weather dynamics, complemented by a feedforward network to handle nonlinear dependencies and complex interactions between weather variables and static information. The proposed index outperforms the benchmark indices and improves average consumer welfare in the joint market relative to the indemnity-only market. These findings offer valuable insights for policymakers, insurers, and policyholders on how risk management innovations can enhance disaster resilience.