Author Archives: Robert Muir-Wood

About Robert Muir-Wood

Chief Research Officer, RMS
Robert Muir-Wood works to enhance approaches to natural catastrophe modeling, identify models for new areas of risk, and explore expanded applications for catastrophe modeling. Recently, he has been focusing on identifying the potential locations and consequences of magnitude 9 earthquakes worldwide. In 2012, as part of Mexico's presidency of the G20, he helped promote government usage of catastrophe models for managing national disaster risks. Robert has more than 20 years of experience developing probabilistic catastrophe models. He was lead author for the 2007 IPCC 4th Assessment Report and 2011 IPCC Special Report on Extremes, is a member of the Climate Risk and Insurance Working Group for the Geneva Association, and is vice-chair of the OECD panel on the Financial Consequences of Large Scale Catastrophes. He is the author of six books, as well as numerous papers and articles in scientific and industry publications. He holds a degree in natural sciences and a PhD in Earth sciences, both from Cambridge University.

The Three “Protection Gaps” (And the Role of Protection Gap Analytics)

The rallying cry has sounded — to “close the protection gap”, the difference between what is paid out by insurance and the total cost of some incident or disaster. Here is an issue that can unite and promote the insurance industry, extending benefits to those in peril by expanding the insurance sector. Having ex-post access to funding after a loss, we know, can bring important benefits.

Yet in reality, there is not just one, but three distinct insurance “protection gaps”, each with separate causes and each requiring different remedies. These protection gaps are so different to one another that we should stop treating them as a single category. Lumping them together can cause confusion.

In this series of four blogs, I will explore each of these three distinct gaps, together with the role of protection gap analytics, and the actions we can plan to address these protection gaps.

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The Emerging Markets Protection Gap

This is the second blog in a series of four blogs examining three potential “protection gaps” and the importance of “protection gap analytics”. To read the first blog post in this series, click here.

Year-by-year, we can check to see if the gap between insured and economic disaster losses in emerging economies is starting to shrink. The gap remains resolutely stuck in the range 80 to 100 percent uninsured. Even a 90 percent average flatters the proportion, as coverage is concentrated in high value hotels, factories and central business districts whereas almost all ordinary houses are without insurance.

We should not be surprised how the emerging markets gap stays so wide.

See what happened in Japan. Unregulated mass rebuilding after the war led to a rising toll of flood disasters. In one single year in the 1950s, more than a million properties were flooded. Then in 1959 there was Typhoon Vera and the Ise Bay storm surge flood catastrophe in which more than 5,000 died. In 1960 the Government declared the level of risk to be intolerable and directed that seven to eight percent of government expenditure should be invested in funding disaster risk reduction. The annual investment proved successful and by the 1980s the annual number of houses flooded had reduced to only three percent of its 1950s level.

For any emerging economy the question can be asked: when did the nation reach the equivalent of Japan in 1960 and start to invest in disaster risk reduction. China passed the point of “intolerable disaster risk” towards the end of the 1990s, while India is undergoing that transition today. This is not just investment in physical disaster risk reduction, but also good risk governance and education.

Insurance is a product of this disaster risk management culture.

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The Intangibles Protection Gap

This is the third blog in a series of four blogs examining three potential “protection gaps” and the importance of “protection gap analytics”. To read the first blog post in this series, click here.

In 1975, 83 percent of the value of the S&P 500 companies was invested in physical assets: factories, refineries, ships and offices. By 2015 that percentage had fallen to 16 percent, leaving 84 percent of the assets as intangible. Intangibles included intellectual property, data on clients, brand value and innovation potential. This massive shift has had huge significance for insurance.

The insurance product was designed to cover tangible risks: first ships and their cargoes, then houses, factories, cars and airplanes. Each item could be independently valued. A claims assessor could be sent out to inspect the damage and measure the costs of repair and replacement.

Now, much of business value is intangible. The “Intangibles Protection Gap” includes all those situations where insurance fails to cover losses suffered by non-physical business assets. How does one assess the value of intangibles — how does one measure loss? Some intellectual property (IP) has been stolen — how much is it worth? You are a cloud service provider hit by a deadly cyberattack which has released some confidential data. What is the value of your lost business, the damage to your reputation and of the penalties levied by the regulator and your customers.

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Protection Gap Analytics: The Role of Risk Modeling

This is the final blog in a series of four blogs examining three potential “protection gaps” and the importance of “protection gap analytics”. To read the first blog post in this series, click here.

We are not going to be able to take effective action to reduce any of these three protection gaps unless we can first learn how to consistently measure the difference between insured and total loss. Such measurement means we can know the current situation as well as set appropriate targets and monitor progress in reducing the gap. It can also help to focus investment and action.

At present, the only form of measurement is to acknowledge the difference between insured loss and the estimated total economic loss once the claims have settled, one or two years after a significant disaster.

In the same way that probabilistic catastrophe risk models were developed to enable insurers and reinsurers to look beyond the latest event loss, so the same models are now required to monitor the protection gap. This is the focus of “protection gap analytics”.

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Schrödinger’s Cat Model

Schrödinger’s cat inhabits a thought-experiment designed to reveal the paradox of quantum properties. A hypothetical cat is sealed in a windowless box, in which there is a device that will administer a lethal poison, according to whether a single atom undergoes radioactive decay. Should the atom decay the cat will be dead. If the atom survives so will the cat. Only the quantum state of the atom is completely unknowable. So, the cat — in principle at least, is half dead and half alive. The simultaneous state of being both alive and dead is called a “superposition”.

While quantum behavior is not an average insurance coverage, (at least until future quantum computer cyber cover emerges), there are situations in the world of risk modeling that come close to Schrödinger’s cat — or perhaps that should better be Schrödinger’s “Cat” (short for Catastrophe)?

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Where Is Insurance in the Post-Grenfell Tower View of Fire Risk?

The first professionals on the stand in the Grenfell Tower Inquiry were the London Fire Brigade, quizzed on their lack of training around managing evacuation from the devastating tower block fire in North Kensington, West London on June 14, 2017. Coming soon, the inquiry’s focus will turn to the architects and fire engineers, the manufacturers of cladding material and the regulatory procedures for determining safety.

Yet, one actor conspicuously missing from this parade of experts is insurance.

You might think that insurance would, by now, be leading the agenda in calculating the fire risk to tower blocks and showing how mitigative action, such as removing or replacing flammable cladding, would directly convert into both lower risk and lower premiums. For this calculation around fire risk has the potential to drive other responses, including which buildings are too dangerous to be habitable.

Yet this calculation cannot come from empirical data on fire losses, as supports most actuarial fire pricing, because this fire is without precedent, at least in the U.K. Instead it will have to be the product of “large building” stochastic fire risk modeling.

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Catastrophe Modeling: The Third Wave of Disruptive Technology

Catastrophe models, conceived in the 1970s and created at the end of the 1980s, have proved to be a “disruptive technology” in reshaping the catastrophe insurance and reinsurance sectors. The first wave of disruption saw the arrival of fresh capital, to found eight new “technical” Bermudan catastrophe reinsurers. The “Class of 1993” included Centre Cat Ltd., Global Capital Re, IPC Re, LaSalle Re, Mid-Ocean Re, Partner Re, Renaissance Re and Tempest Re. Using catastrophe models, these companies were able to set up shop and price hurricane and earthquake contracts without having decades of their own claims history. While only two of these companies survive as independent reinsurers, the legacy of the disruption of 1993 is Bermuda’s sustained dominance in global reinsurance.

A second wave of disruption starting in the mid-1990s saw the introduction of catastrophe bonds: a slow trickle at first but now a steady flow of new structures, as investors who knew nothing about catastrophic loss came to trust modeled risk estimates to establish the bond interest rates and default probabilities. Catastrophe bonds have subsequently undergone their own “Cambrian explosion” into a diverse set of insurance-linked securities (ILS) structures, including those in which the funds go back to supplement reinsurer’s capital. Again, this disruption in accessing novel sources of pension and investment fund capital would have been impossible without catastrophe loss models.

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The Caribbean Disaster Resilience Levy

The islands of the Caribbean have a problem. The air and earth around them is unforgiving. They are some of the most hazardous places on the planet.

What makes many of these islands so beautiful and dramatic also reflects the catastrophic processes that have built the terrain — the earthquakes, eruptions, floods, and landslides. And these catastrophic processes in turn affect the island economies.

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The Mysterious Mitigation Multiple

You will certainly have heard this statement:

“Investing in mitigation action to reduce disaster consequences shows benefits relative to costs multiplied by a factor of X — where X maybe four or seven, or some other number as high as 15.”

As most simply expressed in 2011 by Tom Rooney, U.S. Congressman for Florida’s 17th District “For every US$1 spent on mitigation, US$4 in post-storm cleanup and rebuilding is saved.” And you may have thought — I wonder how they calculated that? But then life is too busy to go into the details, and the statement — that investment in actions to reduce risk shows a fourfold (or sevenfold) reduction in the cost of disasters is very compelling. It implies you could go out and raise the height of a flood wall or strengthen your house and after a few years you would reap a reward in significantly reduced losses.

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The Case of the Trapped Collateral

Was Hurricane Irma in Florida a fire drill for the insurance-linked securities (ILS) and collateralized reinsurance markets — or was this the real thing? In terms of losses, what happened is at the lower end of what the Irma loss in Florida could have become. But what if some of the stuffing had not been knocked out of the storm in Cuba, and if Irma had landed on either the east or west Florida coasts instead of lumbering into the Everglades?

If Irma was a fire drill, then one topic it has highlighted is that faced by “trapped collateral”.

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