LOSS-SAMPLER: Tools for quantifying and managing unseen volatility in 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 (cat)-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, cat-modelling has evolved over the past ~30years 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 cat-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 cat-models, speaks of a service that has yet to realise its full potential.

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

We have created an analytical engine - LOSS-SAMPLER - that utilises contemporary cat-model platforms to broaden these narrow definitions of risk. This allows us to begin uncovering "unknown-knowns" - information that exists in the creation of cat-model analytics but has historically been overlooked because of operational complexities associated with its application.

This analytical engine will be evolved to tackle three specific business challenges for which demand has rapidly increased in the past few years, namely:

1. assisting in the evaluation of cat-models and implementing reliable views of risk
2. informing cat-risk portfolio management
3. quantifying and communicating the impact of climate change

Addressing these challenges will facilitate the building of CER resilience in the global financial system.