Tag Archives: RMS

Managing the Changing Landscape of Terrorism Risk

RMS has released an updated version of its Probabilistic Terrorism Model, which reflects the considerable changes in terrorism risk for Canada, Denmark, Ireland, Italy, and the U.K. as well as the decreased frequency of large-scale-terrorism events for each of the five countries.

To inform the new view of risk, our scientists carried out a comprehensive analysis of global attack and plot data from the past decade. We focused heavily on large-scale attacks – those with the potential to threaten the solvency of an insurer.

The analysis showed that incidents of large-scale attacks have steadily and significantly decreased, which corresponds with a rise in the funding and sophistication of major intelligence agencies in the west.

Our approach to terrorism modeling follows three principles, which have been validated by data on intercepted plots, past successful attacks, and recent intelligence leaks:

  • Effective terrorists seek to achieve optimal results relative to their effort
  • Their actions are highly rational
  • They are highly constrained by pervasive counter-terrorism measures

Of the estimated 200,000 documents taken or leaked by Edward Snowden, one of the most relevant validations of the RMS model is an N.S.A. presentation that explains the routing of international telecommunications traffic. A very significant proportion of international telecommunications traffic is routed through the U.S. and Europe which, coupled with advances in big data analytics and plummeting data storage costs, has made intelligence collection easier and more robust than it has ever been.

 an N.S.A. PRISM presentation explains the routing of international telecommunications traffic

According to available data on the frequency of plots and attacks, the risk of a large-scale attack has been in decline since 2007, but the risk of smaller-scale attacks perpetrated by lone-wolf operatives and homegrown militants remains high.

However, we have learned over the past decade that terrorism risk levels are fluid and can change quickly. With the rise of the Islamic State in Iraq and reports of its successful recruitment of foreigners, as well as ongoing instability in Afghanistan and Pakistan, the risk outlook can change at any moment.

The RMS Probabilistic Terrorism Model incorporates multiple risk outlooks to provide users with the agility to quickly respond to any changes in terrorism risk. RMS is committed to updating its terrorism model as frequently as necessary to provide the most up-to-date, granular, and accurate view of global terrorism risk.

Assessing the Risk of a Global Ebola Pandemic

With the current outbreak of Ebola in western Africa, as well as the recent MERS coronavirus and H7N9 avian flu outbreaks, the world is becoming increasingly concerned about the risk of emerging infectious diseases and their potential to cause the next pandemic.

As catastrophe modelers, how do we assess the risk of a pandemic?

To understand the potential dangers of Ebola, it’s helpful to look to the framework we use at RMS to model infectious disease pandemics. The RMS® LifeRisks Infectious Disease Model projects the excess mortality risk for existing infectious diseases, like influenza, as well as infectious diseases that are emerging or have recently appeared, like Ebola. When modeling a disease, we first look at two main criteria: the virulence and the transmissibility of the pathogen responsible for causing the disease. We then take into account mitigating criteria, including medical and non-medical interventions.


Virulence is a measure of how deadly a disease is, typically measured by the case-fatality rate (CFR), which is the proportion of people who die from the disease to those who do not. The current Ebola CFR is 55 percent. For comparison, the CFR for bubonic plague typically ranges from 25 to 60 percent. CFR for flu is typically less than 0.1 percent.



Transmissibility refers to how likely an infected person is to transmit the disease to another person, and is measured in terms of the basic reproductive number, or R of infection, which is the average number of additional infections one person generates over the course of illness. In order to cause an epidemic, R needs to be greater than 1.

The R for the current Ebola outbreak is greater than 1, and the disease will continue to spread. Past Ebola outbreaks have been estimated to be in the 1.3 to 1.6 range, but have occasionally been greater than 5, which is why there is cause for concern. However, Ebola is less transmissible than many other infectious diseases. For example, measles, which is highly transmissible, has an R of greater than 10 in an unvaccinated environment.


Societal and Environmental Factors

Societal and environmental factors can play a large role in transmissibility. In this case, societal and environmental factors in West Africa have contributed to the disease’s spread. For example, traditional burial practices in which families wash the deceased can expose additional people to the virus.

However, the risk of Ebola developing into a pandemic that extends beyond the region is low, due to the standard public health and infection control practices in place in many countries globally. Ebola can only be transmitted via direct contact with bodily fluids, especially blood, which means that caregivers are the primary people who might be exposed to the virus. In many countries including the U.S., the general practice is to treat all blood as potential sources of infection, due to experience with HIV and other blood-borne diseases. In quarantine situations, such as those being used with the American Ebola cases in Atlanta, the likelihood of transmission from a single person is miniscule.

Medical and Non-Medical Interventions

Medical and non-medical interventions mitigate the risk of an infectious disease pandemic. Typical medical interventions for infectious disease include pharmaceuticals and vaccines. Often, there is no specific therapy or drug available for new or emerging diseases. In these cases, we model the effect of supportive care, which includes management of blood pressure, oxygen, and fluid levels. As we’ve seen with the current outbreak, supportive care and the access to healthcare can vary substantially, depending on the region or population. With the exception of experimental treatments, there are no pharmaceutical interventions available for Ebola. Experimental Ebola drugs are not applicable to large populations at this time.

If there are high enough immunization rates, vaccines can reduce or stop the spread of diseases like measles or whooping cough. Unfortunately, a vaccine isn’t currently available for Ebola. Ebola outbreaks occur sporadically and are caused by different virus strains, making vaccine development more difficult.

In addition to vaccines and medical interventions, we account for non-medical interventions when modeling the impact of pandemics. Non-medical interventions include quarantines, school closures, and travel restrictions. Various countries in Africa have begun to implement these methods in hopes of stopping the spread of Ebola. However, these types of countermeasures can often be difficult to time or enforce properly. Ebola can have an incubation period from two days to as long as 21 days.

So, what is the pandemic potential of Ebola?

The current outbreak is now the largest outbreak of Ebola to date, and the World Health Organization (WHO) has designated the outbreak as a Public Health Emergency of International Concern. However, while cases will continue to develop, a global pandemic is unlikely. Even if the disease were to spread to other regions of the world, Ebola is still considered a rare disease and the transmissibility is likely to be much lower due to quarantine and infection-control measures, even if the CFR remains high. We have not seen any community transmission outside of Africa, and this is expected to continue. Ebola is a very serious disease, with devastating consequences to impacted communities. As risk managers, we aim to improve understanding of catastrophes such as pandemic disease so that as a society we can be better prepared to mitigate risk and recover from catastrophes.

Rebecca Vessenes contributed to this post. As a Senior Quantitative Modeler at RMS, Rebecca is involved in the development and parameterization of the LifeRisks longevity models. She recently completed the longevity model for Japan and has worked on determining the correlation structure for mortality improvement between countries. Prior to working for RMS, she led the Financial Modeling group at AIR. Rebecca earned a Ph.D. in mathematics from California Institute of Technology and is an actuary with the Society of Actuaries.

Disaster Risk Reduction: Catastrophe Modeling Takes the Stage at the United Nations

The UN meeting room at the Palais de Nations in Geneva is oval shaped and more than 100 feet long with curved desks arranged in a series of “U”-shaped configurations. Behind each desk, delegates sit with their placards. On the long desk at the front, from left to right the placards read “IIASA” (a systems research institute based in Austria), “Mexico,” “Japan,” “Netherlands,” and “Risk Management Solutions.”

What was RMS doing on the podium at the UN?

Last month I presented on investing in disaster risk reduction, giving the modeler’s point of view on how risk modeling can be linked with incentivizing actions to reduce the impacts of disasters.
This was a key meeting of what was called “PrepComm,” aimed at coordinating national action for disaster risk reduction. The first such agreement, known as the Hyogo Framework for Action (the HFA), initiated in 2005, is up for renewal in 2015. The plan is to create a tougher and more tangible set of goals and procedures with demonstrable outcomes to reduce the loss of lives, livelihoods, and wealth in disasters.

In some form, catastrophe risk models or modeled outputs are required for setting and monitoring progress in disaster risk reduction. I often use the story of Haiti to make the point: fewer than ten people were killed in earthquakes in Haiti between 1900 and 2009; then in one afternoon in early 2010, an estimated 200,000 people were killed. You cannot use previous disaster data to measure future disaster risk; the underlying distribution of impacts is so skewed, so fat-tailed, and so unknown, that a decade of disaster outcomes reveals nothing about the mean risk.

The UNISDR—the influential UN agency that focuses on disaster risk—recognized the power of probabilistic modeling five years ago. However, it remains hard to communicate that to monitor progress on disaster risk reduction you will have to find some proxies for impacts, or use a model. That was the subject of my address to this session. Borrowing a quote from Michael Bloomberg, sponsor of the Risky Business study for which RMS was the modeler of all the future coastal and hurricane risks: “if you can’t measure it, you can’t manage it.”

The delegate from Algeria was skeptical about how to get the private sector involved in disaster risk reduction. I told the story of Istanbul, where the government makes deals with developers to demolish and reconstruct the most dangerous apartment buildings, rehousing the original occupants while the developer profits from selling extra apartments.

The Philippines wanted to know about empowering local authorities. My answer: get the future risk-based costs of disasters on their balance sheet.

Austria wanted to spread the idea of labeling the risk on every house. The Democratic Republic of Congo wanted to know why conflict is not considered a natural hazard. There were many questions and points of discussion over the course of the meeting.

When the next iteration of HFA arrives in a few weeks time, we will see how all the advice, debate, and consultation from the UN meeting has been digested. Regardless, when governments sign off on the new protocol in Sendai, Japan next March, catastrophe risk modeling is likely to become a core component of the global disaster risk reduction agenda.

Because as Michael Bloomberg said, “If you can’t measure it, you can’t manage it.”

Building Better Models Through Collaboration

To calibrate and validate their models, catastrophe modeling firms ideally have access to large amounts of high-quality, high-resolution claims and exposure data. But the insurance industry has so much to offer than just data.

In addition to exposure data, insurance companies have detailed knowledge of the claims practice itself, the exact policy wording in the underlying exposure, and local expertise. In addition, many insurance companies today have highly experienced teams of scientists that evaluate vendor cat models, or build their own models in-house.

At RMS, our approach to building models has evolved in recent years to capture the insurance industry’s expertise and insight. At the start of a project, we strive to create partnerships with interested clients or companies to ensure we are aligned with market needs. These technical collaborations usually last for the duration of the model development process, and involve regular technical exchanges between RMS and partners to share methodologies and data sources. The exposure and claims data analysis becomes just one part of this broader initiative.

Over the past two years, we has been extremely fortunate to collaborate with two of Japan’s largest primary insurers, Tokio Marine and Sompo Japan, on the development of RMS’ earthquake and typhoon models for Japan.

Collaboration was in full force this June, when RMS’ typhoon modelers met with Hajime Sano, Head of Catastrophe Analytics, Sompo Japan, and his team in Tokyo for a day of technical discussions around hazard and vulnerability. The Sompo modeling team provided interesting ideas around open modeling features within the new model in order to better create their own view of risk.

Hajime Sano also joined us at our Exceedance conference in April as a featured guest speaker. At the event he explained why Sompo Japan develops in-house models, and why they decided to collaborate with RMS on the RMS Japan Typhoon Model update.

Tokio Marine in RMS London Office

At the end of June, Yuki Mizota from Tokio Marine Nichido Fire Insurance and Mizuki Shinohara from Tokio Marine & Nichido Risk Consulting visited our RMS London office. Yuki Mizota gave a very insightful presentation on the market conditions in Japan, and we had a number of productive discussions around exposure, hazard, and vulnerability.

Tour along Arakawa River

One highlight of the collaboration so far with Tokio Marine was a tour along the Arakawa river in Tokyo. During the visit we were able to see firsthand weak spots in the river defense system and witness the new super levees.

Such firsthand intelligence and data is vital in building a strong flood model. We conducted additional research leading to a much deeper understanding of the importance and state of the river defenses in Japan.

Partnerships with Tokio Marine and Sompo Japan

Our partnerships with Tokio Marine and Sompo Japan are great examples of what can be achieved when we work closely with clients to develop and update models. For us and our partners, this collaborative thinking is a win-win: the partner gains deeper insight into RMS modeling methodologies, a key element of their overall enterprise risk management, and RMS is able build better models based on the best science, the best data, and the additional expertise of local insurers. The industry as a whole benefits from state-of-the-art, well-calibrated models built collaboratively by some of the best minds across the insurance community.


RMS and the FIFA World Cup: Insuring Against Terrorism

As we reflect back on this year’s World Cup, which wrapped up without interruption after Germany’s victory on Sunday, it is clear that FIFA’s financial position is much stronger now than in 2006, due in part to the availability of terrorism insurance.

Eleven years ago, the global elite of the soccer world learned about innovative RMS risk analysis to help FIFA to prepare for the 2006 World Cup in Germany. Sponsorship money was essential for FIFA’s cash flow and sponsors insisted on having insurance coverage against event cancellation. After 9/11, terrorism insurance became a necessity, but was available only through Warren Buffet, the astute insurer of last resort, and was extremely expensive. So, FIFA pursued alternative risk transfer to the capital markets through a catastrophe bond.

FIFA’s bankers at Credit Suisse turned to RMS to do what had been thought impossible – to get a terrorism risk securitization rated. It took multiple RMS meetings with Moody’s in London and New York over the course of a year to present and discuss the unique terrorism risk analysis and eventually secure an investment grade rating for Golden Goal Finance Ltd. This $260 million deal remains to this day the only stand-alone securitization of terrorism risk. Prospects for further terrorism risk securitizations depend on the scope of the U.S. Terrorism Risk Insurance Act, which will be renewed at the end of 2014 with some further incremental reduction in the role of the federal government, but RMS was instrumental in instituting the precursors to these prospects.

Securitization of the cancellation risk of the 2006 World Cup was feasible in part due to the national importance of the event, which received extensive counter-terrorism protection.

While cancellation was still the biggest risk this year, the predominant local threat to the World Cup was disruption by public protest and riot. Following the start of the Arab Spring in 2011, there has been a surge of demand for international riot insurance, with a commensurate interest in riot analysis. As with terrorism, security is particularly crucial for the control of riot risk. With 170,000 Brazilian security personnel on duty for the month of the soccer tournament, insurers were able to enjoy the matches without concern that the July 13 final in Rio would be delayed.

While terrorism insurance is more widely available than in the past, it is still in short supply. Expanding modeling capabilities and increased demand for products such as terrorism and riot insurance will result in more insurance-linked securities (ILS) transactions such as the 2006 catastrophe bond, and ultimately promote a more resilient society.

Trading Risk Awards: ILS Innovation Recognized

In June, RMS had the pleasure of hosting a table at the Trading Risk Awards in London. These annual awards aim to recognize the best of the (re)insurance convergence market: individuals and companies contributing to the advancement of the insurance-linked securities (ILS) industry.

That night three RMS-modeled transactions that came to market in 2013 were given special recognition: Tradewynd Series 2013-2, MetroCat Re Ltd., and Atlas IX Capital Ltd. These three transactions incorporate several noteworthy innovations that promise to shape the future of ILS.

Initiative of the year – A Multi-Model Approach to Catastrophe Bond Risk Analysis

AIG and Swiss Re Capital Markets were awarded “Initiative of the Year” for their multi-model approach to Tradewynd Series 2013-2. This transaction provided a first for the industry by introducing transparency to a typically opaque and restricted risk management process.

Typically, data is only privy to the modeling firm retained to produce the risk analysis included in the offering documentation. On this occasion, while RMS was the main modeling agent for the deal, AIG’s exposure data was supplied to all three modeling firms so that investors had a more accurate representation of the risk of the bond under multiple views.

Not only did investors get the RMS view of commercial and high-end residential risk on this bond, they also got unprecedented insights into the exposures driving the risk. The market reacted favorably to the approach with markedly tighter spreads and larger issuance than the prior Tradewynd bond with a nearly identical risk profile.

Non-Life Transaction of the Year – MetroCat Re Ltd

This groundbreaking surge-parametric transaction received two accolades: the Metropolitan Transportation Authority (MTA) was awarded “Sponsor of the Year,” and the deal itself was proclaimed “Non-Life Transaction of the Year,” recognizing the MTA, GC Securities, and Goldman Sachs for their roles in the transaction.

The MetroCat bond addressed the need for surge-insurance capacity after Superstorm Sandy by providing the MTA with insurance cover based on water levels exceeding certain heights at tide gauges in the New York area. The RMS® North Atlantic Hurricane Model, with its full-lifecycle hydrodynamic modeling capability, was critical in understanding the risk to the transaction. The success of MetroCat Re proves that corporates and municipalities can access capital through ILS, as well as produce transactions that provide much needed surge cover.

Life Transaction of the Year – Atlas IX Capital Ltd.

Aon Benfield Securities, BNP Paribas, Natixis, and SCOR won “Life Transaction of the Year” for Atlas IX Capital Ltd., the highest risk bond of its kind to come to market. For this watershed deal, RMS used its suite of LifeRisks models to provide scenario-based modeling results. This allowed investors to gain greater insight into the risk to the transaction from changing trends in baseline mortality in addition to excess mortality from infectious disease, terrorism, earthquakes, and residual risks.

Congratulations to all the winners. We are delighted to see continued innovation in the market.

Is Europe Due for Severe Hailstorms this Summer?

Summer has just started, but weather has already been warm over Europe. Many countries have experienced very high temperatures over the first weeks of June, and there is a chance the 2014 summer will be warmer than normal. A warm atmosphere can bring very high convection potential and potentially lead to a busy severe convective storm season. While seasonal forecasts are uncertain, severe hail events already experienced in June already point to a potential increase in hail risk this year.

The first noticeable hailstorm of the season hit Germany, France, and Belgium between June 7 and 9. Over that period, southern air masses were very warm and clashed with much cooler air from the north. This frontal system brought heavy local wind, rain, and hail, especially over the north of France, Belgium, and northwest region Germany, where large cities like Essen, Düsseldorf, or Köln experienced property damages and six casualties.

RMS scientists Dr. Navin Peiris and Panagiotis Rentzos led a reconnaissance survey in the region a few days after the event and noted that even if there was some evidence of direct hail damage to roofing, most of the substantial damages and transport disruption around Düsseldorf came from tree falls due to very strong wind gusts.

Tree Fallen in Hailstorm

July 12, 2014 will be the 30th anniversary of the most expensive hailstorm in the history of Germany, which generated losses around US$2 billion 1984—half of which was insured. The hailstorm developed amid a streak of late afternoon thunderstorms after a day of intense solar heating. A mass of moist sea air flowed into southern Germany overnight and the combination of moisture and rising air triggered a rapidly intensifying thunderstorm system over the Swiss Mittelland that propagated eastward. Hail fell within a 250-kilometer (150-mile) long and 5–15 kilometer (3–9 mile) wide swath from Lake Constance to eastern Bavaria near the Austrian border. At around 8 p.m. local time, the hailstorm passed over Munich, damaging approximately 70,000 houses, 200,000 cars, 150 aircraft, and most agricultural crops within the storm’s path. More than 400 people were injured. Over half of the insured losses were attributed to damaged cars.

July also marks the first anniversary of the 2013 German hailstorm, which caused insured losses of US$3.4 billion, the second highest from a single natural catastrophe in 2013. Like the June 2014 events, the storm hit after a prolonged period of above-average temperatures in central Europe. The first hail event hit northern Germany on July 27, and the second dropped hailstones with a diameter of up to 8 cm (3.1 in) over south Germany the next day.

Interestingly, all these major events occurred in regions with very high potential of hail damage, which can be described in catastrophe models such as the RMS HailCalc model in terms of kinetic energy to help better manage hail risk. In June, RMS presented the first results of a reconstruction of this hailstorm on at the 1st European Hail Workshop. The paper illustrates how a fast estimation of insured hail losses could be obtained following an event in the future. Developing methods of estimating insured loss totals and return periods immediately after an event are an ongoing area of research in the insurance industry, as illustrated in the RMS paper and others at the workshop.

Hailstorm Image Map