In 2011, the global insurance and reinsurance industry sustained record catastrophe losses of over $100 billion worldwide. From the great earthquake and tsunami in Japan, to ultra liquefaction in New Zealand, to floods in Thailand, to severe tornado and weather events in the United States, the unexpected severity of some of these events took insurers and risk modelers by surprise.
Risk modeling has made remarkable progress since its inception. While no model can ever fully represent the dynamic nature and complexity of the real world, today's models are highly sophisticated tools that provide a wealth of understanding and insight.
Yet the events of 2011 and rising expectations for effective risk management are stretching the status quo, and it's time to step back, learn, and do things differently-and to do so now.
The future of catastrophe modeling is one which makes explicit what we all know intuitively-that catastrophe risk is characterized by uncertainty, learning is ongoing, and that a responsible strategy demands a continuous dialogue between what we know and what we do not.
Resilient risk management offers a new paradigm for understanding and managing risk. It's no longer as simple as using model A, B or C, or some combination thereof.
At RMS, we will always provide our leading scientific and engineering assessment of the risk, serving as a key reference point for dialogue among all stakeholders. But alongside that reference view, you require greater insights into the catastrophe models you rely on, the power to execute your strategy with more insight, and the flexibility to reflect your unique business.
Gain the power to understand the implied bets you are making on the models you use, through deep insights into key assumptions and uncertainties in the reference view of risk.
Quickly adapt to new information and learning, and access plausible alternative views of risk.
Create your own view of the risk if you choose, reflecting the business you write and the policy terms and conditions you apply. One size does not always fit all.