Where Statistics and Data Science Meet Climate Risk Insurance

Yulia Gel, University of Texas at Dallas

Last few years were particularly volatile for the insurance industry in North America and Europe, bringing a record number of claims due to severe weather. According to the 2013 World Bank study, annual average losses from natural disasters have increased from $50 billion in the 1980s to about $200 billion nowadays. Adaptation to such changes requires early recognition of vulnerable areas and the extent of the future risk due to weather factors. Despite the well documented impact of climate change on the insurance sector, there exists a relatively limited number of studies addressing the effect of the so-called ``normal'' extreme weather (i.e., higher frequency, lower individual but high cumulative impact events) on the insurance dynamics. In this talk we discuss utility and limitations of statistical and machine learning procedures to address modelling and forecasting of such weather-related insurance losses and the potential impact of uncertainty quantification on the insurance sector and policy holders.