ASRM Seminar Series: Dr. Shimeng Huang (Purdue University)

Distributional Deep Learning for Flood Risk under Compound Climate Events: An Application to Index Insurance 

Catastrophic flood events are increasingly driven by compound climate processes, where interactions across atmospheric, hydrological, and geographic systems generate complex and spatially structured loss patterns. These features pose challenges for property-level loss modeling and limit the effectiveness of traditional risk management approaches. We develop a hierarchical distributional deep learning framework that captures temporal evolution of weather conditions, spatial structure induced by geographic proximity and hydrological connectivity, and heterogeneous exposure characteristics. The model integrates sequence-based representations, graph-based spatial learning, and permutation-invariant aggregation of property-level features, and parameterizes the full conditional loss distribution using a generalized gamma family.

Using data from named flood events under the National Flood Insurance Program, we show that the proposed approach improves predictive performance at both the property and portfolio levels relative to existing methods, including under forward-looking evaluation. Beyond prediction, we demonstrate the value of distributional modeling through an application to index insurance design for catastrophic risk management. These findings illustrate how improved modeling of losses arising from compound climate events can support more effective and responsive risk mitigation strategies.

About the Speaker

Shimeng Huang is an assistant professor of actuarial science in the Department of Statistics and the Department of Mathematics at Purdue University. She received her PhD in Insurance Economics and Actuarial Analytics from the University of Wisconsin–Madison in 2025 and is an Associate of the Society of Actuaries (ASA). Her research lies at the intersection of climate risk, catastrophic events, insurance analytics, and dependence modeling. She develops statistical and machine learning methods and financial instruments for managing the economic impacts of disasters.