LOSS-SAMPLER: A tool for identifying unseen volatility in present and future Climate and Environmental Risk (CER) portfolios

The combined threat of climate and weather perils to annual global GDP is over $130billion (Cambridge Centre for Risk Studies, 2020), making them the largest of _any_ conceivable threat to GDP, whether man-made or natural in origin. In the face of such severe Climate and Environmental Risk (CER), catastrophe modelling has become indispensable to (re)insurers as a risk-management tool, and has begun to usher in a new era of improved understanding of risk.

Originating in the late 1980s, catastrophe modelling has evolved over the past ~30 years to help tackle some of the most complex challenges facing the long-term sustainability of global society. In his speech on Climate Change and Financial Stability, the then Governor of the Bank of England directly praised the models, stating that "UK insurers withstood the events of 2011, one of the worst years on record for insurance losses. Your models were validated, claims were paid, and solvency was maintained" (Mark Carney, 2015).

While there have undoubtedly been many benefits to society from the advent of catastrophe modelling, we are far from extracting its maximum value to private industry and society. Our capacity to still be shocked by catastrophic events that are not just easily conceivable, but well-represented in contemporary catastrophe models, speaks of a service that is not even close to realising its full potential.

The field of catastrophe modelling is still extremely young compared to the many longstanding academic disciplines whose data and information feed its processes, which may explain why much of the potential value currently remains untapped. Somewhat unfortunately, over the past three decades the catastrophe modelling industry has primarily focused on deploying analytics that focus on a narrow definition of risk - that is, a definition that is easily operationalized in risk pricing and capital management situations.

Here we create a tool that utilises contemporary catastrophe model output to begin to uncover many more "unknown-knowns" - information that exists in the creation of catastrophe model analytics, but has historically been overlooked because of operational complexities associated with its application. The tool will target two key business applications for which demand has rapidly increased in the past few years, namely:

1\. Informing present-day catastrophe-risk portfolio re-structuring decisions.

2.Quantifying and communicating the impact of climate change.

Both of these applications will ultimately facilitate the building of resilience to CER in private industry and broader society.