  As climate change poses new and more unpredictable challenges to society,
insurance is an essential avenue to protect against loss caused by extreme
events. Traditional insurance risk models employ statistical analyses that are
inaccurate and are becoming increasingly flawed as climate change renders
weather more erratic and extreme. Data-driven parametric insurance could
provide necessary protection to supplement traditional insurance. We use a
technique referred to as the deep sigma point process, which is one of the
Bayesian neural network approaches, for the data analysis portion of parametric
insurance using residential internet connectivity dropout in US as a case
study. We show that our model has significantly improved accuracy compared to
traditional statistical models. We further demonstrate that each state in US
has a unique weather factor that primarily influences dropout rates and that by
combining multiple weather factors we can build highly accurate risk models for
parametric insurance. We expect that our method can be applied to many types of
risk to build parametric insurance options, particularly as climate change
makes risk modeling more challenging.
