In the fifth part of our swan blog series, we explore how black swans and gray rhinos may interact in a changing landscape, and how insurers can respond in such an uncertain environment.
What are gray rhinos?
Coined by author Michele Wucker, gray rhinos are highly probable, high-impact threats that are often overlooked. Imagine walking through a foggy morning in the savannah – you could spot a rhino if you were looking. But without paying attention, you might not notice it until it crashes into you, leaving no time to react.
As climate change continues to transform the natural world, many animals are rewriting the rhythms of their lives in response. Earlier breeding, shifting migration patterns, exploration of new territories and, of course, creeping threats of extinction.
In the previous four parts of this blog series, we have incorporated the idea of using black swans to represent risks previously thought impossible, and gray rhinos to represent highly probably, high impact yet neglected threats. Like the behavioural transformations observable in the animal kingdom, climate-related risks are developing, and this includes inter-risk relationships.
While we recognise that climate risk impacts both the assets and liabilities of a life insurer, this series focuses on liability risks. In this article, demographic-demographic and market-demographic risk relationships are covered, while market-market relationships are excluded.
In part 3 of this series we explored the potential impact of climate change on policyholder behaviour. In part 4 we explored the direct and indirect impacts of climate change on mortality and morbidity risks. In this penultimate blog we explore how black swans and gray rhinos may interact with one another in a changing landscape, and how insurers can begin to grasp their aggregated risk exposures in such an uncertain landscape. Risk aggregation is the process of understanding how a business’s material risks interact, and using these relationships to gain a holistic view of the overall risk exposure. This process can be used to explore sensitivities, diversification benefits, and concentration risks.
Life insurers measure these inter-risk relationships in different ways, including simple correlation matrices, copula models to define non-linear and tail dependencies, proxy models or ‘economic scenario generators’, and expert judgement. Historical data is often used, where available, to inform the analysis.
In comparison to non-life insurers, who are impacted more directly in the short term by physical risks, life insurers are more likely to be impacted by long-term chronic climate effects1. For a life insurer, climate-related liability risks are predominantly indirect, long-term, systemic and forward-looking. This means less data now, and significant uncertainty not only about how risks react along different climate pathways2, but how the interactions between risks may develop. How can life insurers respond to this uncertainty? To do nothing in the face of this information drought, however, would be the equivalent of preparing for the herd of gray rhinos without binoculars. In SS5/25, the Prudential Regulation Authority (PRA) noted that access to reliable, relevant and sufficient data is a major hurdle for life insurers in conducting thorough and meaningful climate-related risk analysis. While the PRA emphasises that firms should be developing plans to address this data gap, we can also consider the value of holistic, qualitative exploratory analysis in the meantime. Qualitative approaches can include gathering expert judgement views, exploring climate scenarios, drawing risk heat maps, and thematic reviews.
Qualitative analysis can be particularly helpful in risk aggregation. There is value to be found in theoretically exploring the possible routes to shifts in risk relationships given what we do know about future climate pathways, paired with historical events like economic shocks and pandemics. Here we will give some examples of how these potential relationship shifts may happen.
Above we have given some examples of how climate change can impact the relationships between risks, both demographic and market. While we don’t have the historical data sets to analyse these correlations across different climate pathways, it is still meaningful to qualitatively explore how they may change, and gain a holistic view of how sensitive the balance sheet is to systemic shifts in inter-risk relationships.
Examples of the explorative analysis life insurers can do:
The options for exploratory analysis are numerous and potentially rich in insights, and reinforce the message that we cannot let the lack of historical data be a blocker to climate change preparedness. Returning to the savannah: keep your eyes fixed on the horizon, and be prepared to believe your eyes when you start to see changes in rhino and swan behaviour you previously thought were impossible.
