Flood Risk Financing Gap in Malaysia: A Monte Carlo Simulation Analysis across Regional Archetypes

Abstract

Flooding in Malaysia presents a growing financial challenge, with a substantial proportion of losses remaining uninsured and largely dependent on post-disaster government assistance. This study aims to assess the flood risk profile and quantify the financing gap across regional rainfall archetypes using a probabilistic modeling approach. A Monte Carlo simulation with 10,000 iterations was employed to generate potential annual flood loss scenarios, incorporating projected urbanization growth and stochastic variability. Key risk metrics, namely Expected Annual Loss (EAL) and Probable Maximum Loss (PML) at the 99.5th percentile, were estimated to evaluate financial exposure. The results indicate significant variation in loss distributions across six regional groups, reflecting differences in rainfall intensity, exposure levels, and elasticity characteristics. At the national level, the EAL is estimated at RM2.63 billion, while the PML reaches RM8.07 billion, resulting in a financing gap of RM5.43 billion. The findings further reveal a right-skewed loss distribution with substantial tail risk concentrated in high-exposure regions. Additionally, diversification effects reduce overall national risk compared to aggregated regional extremes. The study highlights the limitations of deterministic approaches and demonstrates the importance of probabilistic modeling in disaster risk financing. It also emphasizes the need for risk-based premium structures, strengthened reinsurance arrangements, and integrated public–private financing mechanisms to enhance national financial resilience against flood events.