Building in resilience

For more than 20 years, catastrophe risk modellers have provided the insurance and reinsurance industry with valuable tools to quantify risk, and the market is more robust today as a result. Re/ insurers—insurers and reinsurers—have steadily integrated model-based analytics into their business processes, with many model-savvy industry leaders raising the bar on the appropriate use and application of models to support core business decisions.

Despite this notable progress, we now fi nd ourselves at an inflection point in the development, dialogue, and use of catastrophe models. In 2011, the global re/insurance industry suffered $100 billion of worldwide catastrophe losses, stemming from earthquakes in Japan and New Zealand, floods in Thailand, and severe tornado and weather events in the US. On top of record-breaking losses, several of the events and their consequences came as a surprise to modellers and re/insurers alike.

Additionally, RMS delivered major changes to our models last year, including the introduction of new models for North Atlantic hurricane and Europe windstorm. They suggested a significant increase in the view of risk for most portfolios, with the magnitude of change surprising many in the industry.

There is a broad consensus that catastrophe models, used responsibly and insightfully, are valuable risk management tools. Yet last year’s surprises and rising expectations for effective risk management have stretched the status quo. While models can help optimise a book of business, an overreliance on models can lead to fragile portfolios that are prone to surprises, either from Mother Nature or from the models themselves.

Currently, there is too much latency and obstruction between scientific knowledge and data and executives making decisions on how to price and manage their risks. As a result, a paradigm shift is required in the understanding and use of catastrophe models. This paradigm shift is now underway at RMS—we call it ‘resilience’.

The shift is about changing how we build and deliver our models, as well as how we provide solutions that enable re/insurers to understand their implied bets, take control of key assumptions, and implement resilient risk management strategies that are informed by sophisticated models, but not dependent on them.

What is resilience?

Simply put, it is an approach to risk management that makes explicit what we all know intuitively—that catastrophe risk is characterised by deep uncertainty, and a responsible strategy demands a continuous dialogue between what we know and what we do not know. In a resilient framework, a company not only seeks to maximise its returns based on its view of the risk landscape, but looks to minimise its regret given the uncertainties in the maps that describe the terrain ahead.

To make this work, three things are required. Re/insurers need to be able to (i) understand their implied bets; (ii) take control of the key assumptions driving their risk; and (iii) manage not only the risk-return tradeoffs from what we think we know, but the potential regret from what we suspect, and what we do not.

“Embracing new technologies will enable us to deliver and transform the market’s ability to access the speed, transparency, and nimbleness required to implement this new paradigm.”

Research and development practices have to become less rigid, with less delay between the acquisition of new knowledge and the delivery of new models. Products should offer a broad view into alternative perspectives on risk, and re/insurance companies must have the power to understand the key model assumptions that drive those outputs. It’s not about whether one uses model X, Y, or Z (or some combination thereof), it’s about using models as tools to understand the implied bets and sensitivities, and then take control of key assumptions.

Doing this correctly requires a modelling environment in which models are constantly used to explore the relationships between risks and assumptions, coupled tightly to a decision-making environment that is powered by realtime analytic technology that helps inform risk-return-regret tradeoffs. It’s not about simply ‘running the model’ and then ‘generating the report’.

The same exposures will look different to different companies, and strategies will be driven by explicit choices, and to the degree of resiliency one chooses. As always, the choices regarding risk and return are up to each company.

Resilient risk management offers a new archetype for understanding and managing risk, and requires new attitudes and behaviours, new technology and solutions, and new business practices.