Bridging the Disaster Protection Gap with Index Insurance
Date: Wednesday, 13 May 2026
Time: 10.30 am – 12 pm
Venue: Lecture Theatre 3 (ABS-02-LT3)
Chairperson: Assoc Prof Jinggong Zhang
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.
About the Speaker
Shimeng Huang is an Assistant Professor of Actuarial Science in the Departments of Statistics and Mathematics at Purdue University. She earned her Ph.D. in Insurance Economics and Actuarial Analytics from the University of Wisconsin–Madison in 2025. She is a proud alumna of Nanyang Business School at Nanyang Technological University, where she received a Bachelor of Business and Accountancy in 2020. Her research sits at the intersection of climate risk, catastrophic events, insurance analytics, machine learning, and dependence modeling, aiming to develop innovative tools for managing the economic impacts of disasters.