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Helen Yates
May 05, 2020
Climate Change: The Cost of Inaction

With pressure from multiple directions for a change in the approach to climate risk, how the insurance industry responds is under scrutiny Severe threats to the climate account for all of the top long-term risks in this year’s World Economic Forum (WEF) “Global Risks Report.” For the first time in the survey’s 10-year outlook, the top five global risks in terms of likelihood are all environmental. From an industry perspective, each one of these risks has potentially significant consequences for insurance and reinsurance companies: Extreme weather events with major damage to property, infrastructure and loss of human life Failure of climate change mitigation and adaptation by governments and businesses Man-made environmental damage and disasters including massive oil spills and incidents of radioactive contamination Major biodiversity loss and ecosystem collapse (terrestrial or marine) with irreversible consequences for the environment, resulting in severely depleted resources for humans as well as industries Major natural disasters such as earthquakes, tsunamis, volcanic eruptions and geomagnetic storms “There is mounting pressure on companies from investors, regulators, customers and employees to demonstrate their resilience to rising climate volatility,” says John Drzik, chairman of Marsh and McLennan Insights. “Scientific advances mean that climate risks can now be modeled with greater accuracy and incorporated into risk management and business plans. High-profile events, like recent wildfires in Australia and California, are adding pressure on companies to take action on climate risk.” There is mounting pressure on companies from investors, regulators, customers and employees to demonstrate their resilience to rising climate volatility”  John Drzik Marsh and McLennan Insights In December 2019, the Bank of England introduced new measures for insurers, expecting them to assess, manage and report on the financial risks of climate change as part of the bank’s 2021 Biennial Exploratory Scenario (BES) exercise. The BES builds on the Prudential Regulatory Authority’s Insurance Stress Test 2019, which asked insurers to stress test their assets and liabilities based on a series of future climate scenarios. The Network for the Greening of the Financial System shows how regulators in other countries are moving in a similar direction. “The BES is a pioneering exercise, which builds on the considerable progress in addressing climate-related risks that has already been made by firms, central banks and regulators,” said outgoing Bank of England governor Mark Carney. “Climate change will affect the value of virtually every financial asset; the BES will help ensure the core of our financial system is resilient to those changes.” The insurance industry’s approach to climate change is evolving. Industry-backed groups such as ClimateWise have been set up to respond to the challenges posed by climate change while also influencing policymakers. “Given the continual growth in exposure to natural catastrophes, insurance can no longer simply rely on a strategy of assessing and re-pricing risk,” says Maurice Tulloch, former chair of ClimateWise and CEO of international insurance at Aviva. “Doing so threatens a rise of uninsurable markets.” The Cost of Extreme Events In the past, property catastrophe (re)insurers were able to recalibrate their perception of natural catastrophe risk on an annual basis, as policies came up for renewal, believing that changes to hazard frequency and/or severity would occur incrementally over time. However, it has become apparent that some natural hazards have a much greater climate footprint than had been previously imagined. Attribution studies are helping insurers and other stakeholders to measure the financial impact of climate change on a specific event. “You have had events in the last few years that have a climate change signature to them,” says Robert Muir-Wood, chief research officer of science and technology at RMS. “That could include wildfire in California or extraordinary amounts of rainfall during Hurricane Harvey over Houston, or the intensity of hurricanes in the Caribbean, such as Irma, Maria and Dorian. “These events appear to be more intense and severe than those that have occurred in the past,” he continues. “Attribution studies are corroborating the fact that these natural disasters really do have a climate change signature. It was a bit experimental to start with, but now it’s just become a regular part of the picture, that after every event a designated attribution study program will be undertaken … often by more than one climate lab. “In the past it was a rather futile argument whether or not an event had a greater impact because of climate change, because you couldn’t really prove the point,” he adds. “Now it’s possible to say not only if an event has a climate change influence, but by how much. The issue isn’t whether something was or was not climate change, it’s that climate change has affected the probability of an event like that by this amount. That is the nature of the conversation now, which is an intelligent way of thinking about it.” Now it’s possible to say not only if an event has a climate change influence, but by how much. The issue isn’t whether something was or was not climate change, it’s that climate change has affected the probability of an event like that by this amount  Robert Muir-Wood RMS Record catastrophe losses in 2017 and 2018 — with combined claims costing insurers US$230 billion, according to Swiss Re sigma — have had a significant impact on the competitive and financial position of many property catastrophe (re)insurers. The loss tally from 2019 was less severe, with global insurance losses below the 10-year average at US$56 billion, but Typhoons Faxai and Hagibis caused significant damage to Japan when they occurred just weeks apart in September and October. “It can be argued that the insurance industry is the only sector that is going to be able to absorb the losses from climate change,” adds Muir-Wood. “Companies already feel they are picking up losses in this area and it’s a bit uncharted — you can’t just use the average of history. It doesn’t really work anymore. So, we need to provide the models that give our clients the comfort of knowing how to handle and price climate change risks in anticipation.” The Cost of Short-Termism While climate change is clearly on the agenda of the boards of international insurance and reinsurance firms, its emphasis differs from company to company, according to the Geneva Association. In a report, the industry think tank found that insurers are hindered from scaling up their contribution to climate adaptation and mitigation by barriers that are imposed at a public policy and regulatory level. The need to take a long-term view on climate change is at odds with the pressures that insurance companies are under as public and regulated entities. Shareholder expectations and the political demands to keep insurance rates affordable are in conflict with the need to charge a risk-adjusted price or reduce exposures in regions that are highly catastrophe exposed. Examples of this need to protect property owners from full risk pricing became an election issue in the Florida market when state-owned carrier Florida Citizens supported customers with effectively subsidized premiums. The disproportionate emphasis on using the historical record as a means of modeling the probability of future losses is a further challenge for the private market operating in the state. “In the past when insurers were confronted with climate change, they were comfortable with the sense that they could always put up the price or avoid writing the business if the risk got too high,” says Muir-Wood. “But I don’t think that’s a credible position anymore. We see situations, such as in California, where insurers are told they should already have priced in climate change risk and they need to use the average of the last 30 years, and that’s obviously a challenge for the solvency of insurers. Regulators want to be up to speed on this. If levels of risk are increasing, they need to make sure that (re)insurance companies can remain solvent. That they have enough capital to take on those risks. “The Florida Insurance Commissioner’s function is more weighted to look after the interests of consumers around insurance prices, and they maintain a very strong line that risk models should be calibrated against the long-term historical averages,” he continues. “And they’ve said that both in Florida for hurricane and in California for wildfire. And in a time of change and a time of increased risk, that position is clearly not in the interest of insurers, and they need to be thinking carefully about that. “Regulators want to be up to speed on this,” he adds. “If levels of risk are increasing, they need to make sure that (re)insurance companies can remain solvent. That they have enough capital to take on those risks. And supervisors will expect the companies they regulate to turn up with extremely good arguments and a demonstration of the data behind their position as to how they are pricing their risk and managing their portfolios.” The Reputational Cost of Inaction Despite the persistence of near-term pressures, a lack of action and a long-term view on climate change is no longer a viable option for the industry. In part, this is due to a mounting reputational cost. European and Australian (re)insurers have, for instance, been more proactive in divesting from fossil fuels than their American and Asian counterparts. This is expected to change as negative attention mounts in both mainstream and social media. The industry’s retreat from coal is gathering pace as public pressure on the fossil fuel industry and its supporters grows. The number of insurers withdrawing cover for coal more than doubled in 2019, with coal exit policies announced by 17 (re)insurance companies. “The role of insurers is to manage society’s risks — it is their duty and in their own interest to help avoid climate breakdown,” says Peter Bosshard, coordinator of the Unfriend Coal campaign. “The industry’s retreat from coal is gathering pace as public pressure on the fossil fuel industry and its supporters grows.” The influence of climate change activists such as Greta Thunberg, the actions of NGO pressure groups like Unfriend Coal and growing climate change disclosure requirements are building a critical momentum and scrutiny into the action (or lack thereof) taken by insurance senior management. “If you are in the driver’s seat of an insurance company and you know your customers’ attitudes are shifting quite fast, then you need to avoid looking as though you are behind the curve,” says Muir-Wood. “Quite clearly there is a reputational side to this. Attitudes are changing, and as an industry we should anticipate that all sorts of things that are tolerated today will become unacceptable in the future.” To understand your organization’s potential exposure to climate change contact the RMS team here

ANTONY IRELAND
May 05, 2020
Severe Convective Storms: Experience Cannot Tell the Whole Story

Severe convective storms can strike with little warning across vast areas of the planet, yet some insurers still rely solely on historical records that do not capture the full spectrum of risk at given locations. EXPOSURE explores the limitations of this approach and how they can be overcome with cat modeling Attritional and high-severity claims from severe convective storms (SCS) — tornadoes, hail, straight-line winds and lightning — are on the rise. In fact, in the U.S., average annual insured losses (AAL) from SCS now rival even those from hurricanes, at around US$17 billion, according to the latest RMS U.S. SCS Industry Loss Curve from 2018. In Canada, SCS cost insurers more than any other natural peril on average each year. Despite the scale of the threat, it is often overlooked as a low volatility, attritional peril  Christopher Allen RMS “Despite the scale of the threat, it is often overlooked as a low volatility, attritional peril,” says Christopher Allen, product manager for the North American SCS and winterstorm models at RMS. But losses can be very volatile, particularly when considering individual geographic regions or portfolios (see Figure 1). Moreover, they can be very high. “The U.S. experiences higher insured losses from SCS than any other country. According to the National Weather Service Storm Prediction Center, there over 1,000 tornadoes every year on average. But while a powerful tornado does not cause the same total damage as a major earthquake or hurricane, these events are still capable of causing catastrophic losses that run into the billions.” Figure 1: Insured losses from U.S. SCS in the Northeast (New York, Connecticut, Rhode Island, Massachusetts, New Hampshire, Vermont, Maine), Great Plains (North Dakota, South Dakota, Nebraska, Kansas, Oklahoma) and Southeast (Alabama, Mississippi, Louisiana, Georgia). Losses are trended to 2020 and then scaled separately for each region so the mean loss in each region becomes 100. Source: Industry Loss Data Two of the costliest SCS outbreaks to date hit the U.S. in spring 2011. In late April, large hail, straight-line winds and over 350 tornadoes spawned across wide areas of the South and Midwest, including over the cities of Tuscaloosa and Birmingham, Alabama, which were hit by a tornado rating EF-4 on the Enhanced Fujita (EF) scale. In late May, an outbreak of several hundred more tornadoes occurred over a similarly wide area, including an EF-5 tornado in Joplin, Missouri, that killed over 150 people. If the two outbreaks occurred again today, according to an RMS estimate based on trending industry loss data, each would easily cause over US$10 billion of insured loss. However, extreme losses from SCS do not just occur in the U.S. In April 1999, a hailstorm in Sydney dropped hailstones of up to 3.5 inches (9 centimeters) in diameter over the city, causing insured losses of AU$5.6 billion according to the Insurance Council of Australia (ICA), currently the most costly insurance event in Australia’s history [1]. “It is entirely possible we will soon see claims in excess of US$10 billion from a single SCS event,” Allen says, warning that relying on historical data alone to quantify SCS (re)insurance risk leaves carriers underprepared and overexposed. Historical Records are Short and Biased According to Allen, the rarity of SCS at a local level means historical weather and loss data fall short of fully characterizing SCS hazard. In the U.S., the Storm Prediction Center’s national record of hail and straight-line wind reports goes back to 1955, and tornado reports date back to 1950. In Canada, routine tornado reports go back to 1980. “These may seem like adequate records, but they only scratch the surface of the many SCS scenarios nature can throw at us,” Allen says. “To capture full SCS variability at a given location, records should be simulated over thousands, not tens, of years,” he explains. “This is only possible using a cat model that simulates a very wide range of possible storms to give a fuller representation of the risk at that location. Observed over tens of thousands of years, most locations would have been hit by SCS just as frequently as their neighbors, but this will never be reflected in the historical records. Just because a town or city has not been hit by a tornado in recent years doesn’t mean it can’t be.” To capture full SCS variability at a given location, records should be simulated over thousands, not tens, of years Shorter historical records could also misrepresent the severity of SCS possible at a given location. Total insured catastrophe losses in Phoenix, Arizona, for example, were typically negligible between 1990 and 2009, but on October 5, 2010, Phoenix was hit by its largest-ever tornado and hail outbreak, causing economic losses of US$4.5 billion. (Source: NOAA National Centers for Environmental Information) Just like the national observations, insurers’ own claims histories, or industry data such as presented in Figure 1, are also too short to capture the full extent of SCS volatility, Allen warns. “Some primary insurers write very large volumes of natural catastrophe business and have comprehensive claims records dating back 20 or so years, which are sometimes seen as good enough datasets on which to evaluate the risk at their insured locations. However, underwriting based solely on this length of experience could lead to more surprises and greater earnings instability.” If a Tree Falls and No One Hears… Historical SCS records in most countries rely primarily on human observation reports. If a tornado is not seen, it is not reported, which means that unlike a hurricane or large earthquake it is possible to miss SCS in the recent historical record. “While this happens less often in Europe, which has a high population density, missed sightings can distort historical data in Canada, Australia and remote parts of the U.S.,” Allen explains. Another key issue is that the EF scale rates tornado strength based on how much damage is caused, but this does not always reflect the power of the storm. If a strong tornado occurs in a rural area with few buildings, for example, it won’t register high on the EF scale, even though it could have caused major damage to an urban area. “This again makes the historical record very challenging to interpret,” he says. “Catastrophe modelers invest a great deal of time and effort in understanding the strengths and weaknesses of historical data. By using robust aspects of observations in conjunction with other methods, for example numerical weather simulations, they are able to build upon and advance beyond what experience tells us, allowing for more credible evaluation of SCS risk than using experience alone.” Then there is the issue of rising exposures. Urban expansion and rising property prices, in combination with factors such as rising labor costs and aging roofs that are increasingly susceptible to damage, are pushing exposure values upward. “This means that an identical SCS in the same location would most likely result in a higher loss today than 20 years ago, or in some cases may result in an insured loss where previously there would have been none,” Allen explains. Calgary, Alberta, for example, is the hailstorm capital of Canada. On September 7, 1991, a major hailstorm over the city resulted in the country’s largest insured loss to date from a single storm: CA$343 million was paid out at the time. The city has of course expanded significantly since then (see Figure 2), and the value of the exposure in preexisting urban areas has also increased. An identical hailstorm occurring over the city today would therefore cause far larger insured losses, even without considering inflation. Figure 2: Urban expansion in Calgary, Alberta, Canada. European Space Agency. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf “Probabilistic SCS cat modeling addresses these issues,” Allen says. “Rather than being constrained by historical data, the framework builds upon and beyond it using meteorological, engineering and insurance knowledge to evaluate what is physically possible today. This means claims do not have to be ‘on-leveled’ to account for changing exposures, which may require the user to make some possibly tenuous adjustments and extrapolations; users simply input the exposures they have today and the model outputs today’s risk.” The Catastrophe Modeling Approach In addition to their ability to simulate “synthetic” loss events over thousands of years, Allen argues, cat models make it easier to conduct sensitivity testing by location, varying policy terms or construction classes; to drill into loss-driving properties within portfolios; and to optimize attachment points for reinsurance programs. SCS cat models are commonly used in the reinsurance market, partly because they make it easy to assess tail risk (again, difficult to do using a short historical record alone), but they are currently used less frequently for underwriting primary risks. There are instances of carriers that use catastrophe models for reinsurance business but still rely on historical claims data for direct insurance business. So why do some primary insurers not take advantage of the cat modeling approach? “Though not marketwide, there can be a perception that experience alone represents the full spectrum of SCS risk — and this overlooks the historical record’s limitations, potentially adding unaccounted-for risk to their portfolios,” Allen says. What is more, detailed studies of historical records and claims “on-leveling” to account for changes over time are challenging and very time-consuming. By contrast, insurers who are already familiar with the cat modeling framework (for example, for hurricane) should find that switching to a probabilistic SCS model is relatively simple and requires little additional learning from the user, as the model employs the same framework as for other peril models, he explains. A US$10 billion SCS loss is around the corner, and carriers need to be prepared and have at their disposal the ability to calculate the probability of that occurring for any given location Furthermore, catastrophe model data formats, such as the RMS Exposure and Results Data Modules (EDM and RDM), are already widely exchanged, and now the Risk Data Open Standard™ (RDOS) will have increasing value within the (re)insurance industry. Reinsurance brokers make heavy use of cat modeling submissions when placing reinsurance, for example, while rating agencies increasingly request catastrophe modeling results when determining company credit ratings. Allen argues that with property cat portfolios under pressure and the insurance market now hardening, it is all the more important that insurers select and price risks as accurately as possible to ensure they increase profits and reduce their combined ratios. “A US$10 billion SCS loss is around the corner, and carriers need to be prepared and have at their disposal the ability to calculate the probability of that occurring for any given location,” he says. “To truly understand their exposure, risk must be determined based on all possible tomorrows, in addition to what has happened in the past.” [1] Losses normalized to 2017 Australian dollars and exposure by the ICA. Source: https://www.icadataglobe.com/access-catastrophe-data. To obtain a holistic view of severe weather risk contact the RMS team here

NIGEL ALLEN
May 05, 2020
Breaking Down the Pandemic

As COVID-19 has spread across the world and billions of people are on lockdown, EXPOSURE looks at how the latest scientific data can help insurers better model pandemic risk The coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. In a matter of months, it has expanded from the first reported cases in the city of Wuhan in Hubei province, China, to confirmed cases in over 200 countries around the globe. At the time of writing, approximately one-third of the world’s population is in some form of lockdown, with movement and activities restricted in an effort to slow the disease’s spread. The transmissibility of COVID-19 is truly global, with even the extreme remoteness of location proving no barrier to its relentless progression as it reaches far-flung locations such as Papua New Guinea and Timor-Leste. After declaring the event a global pandemic, Dr. Tedros Adhanom Ghebreyesus, WHO director general, said: “We have never before seen a pandemic sparked by a coronavirus. This is the first pandemic caused by a coronavirus. And we have never before seen a pandemic that can be controlled. … This is not just a public health crisis, it is a crisis that will touch every sector — so every sector and every individual must be involved in the fight.” Ignoring the Near Misses COVID-19 has been described as the biggest global catastrophe since World War II. Its impact on every part of our lives, from the mundane to the complex, will be profound, and its ramifications will be far-reaching and enduring. On multiple levels, the coronavirus has caught the world off guard. So rapidly has it spread that initial response strategies, designed to slow its progress, were quickly reevaluated and more restrictive measures have been required to stem the tide. Yet, some are asking why many nations have been so flat-footed in their response. To find a comparable pandemic event, it is necessary to look back over 100 years to the 1918 flu pandemic, also referred to as Spanish flu. While this is a considerable time gap, the interim period has witnessed multiple near misses that should have ensured countries remained primed for a potential pandemic. “For very good reasons, people are categorizing COVID-19 as a game-changer. However, SARS in 2003 should have been a game-changer, MERS in 2012 should have been a game-changer, Ebola in 2014 should have been a game-changer. If you look back over the last decade alone, we have seen multiple near misses.” Dr. Gordon Woo RMS However, as Dr. Gordon Woo, catastrophist at RMS, explains, such events have gone largely ignored. “For very good reasons, people are categorizing COVID-19 as a game-changer. However, SARS in 2003 should have been a game-changer, MERS in 2012 should have been a game-changer, Ebola in 2014 should have been a game-changer. If you look back over the last decade alone, we have seen multiple near misses. “If you examine MERS, this had a mortality rate of approximately 30 percent — much greater than COVID-19 — yet fortunately it was not a highly transmissible virus. However, in South Korea a mutation saw its transmissibility rate surge to four chains of infection, which is why it had such a considerable impact on the country.” While COVID-19 is caused by a novel virus and there is no preexisting immunity within the population, its genetic makeup shares 80 percent of the coronavirus genes that sparked the 2003 SARS outbreak. In fact, the virus is officially titled “severe acute respiratory syndrome coronavirus 2,” or “SARS-CoV-2.” However, the WHO refers to it by the name of the disease it causes, COVID-19, as calling it SARS could have “unintended consequences in terms of creating unnecessary fear for some populations, especially in Asia which was worst affected by the SARS outbreak in 2003.” “Unfortunately, people do not respond to near misses,” Woo adds, “they only respond to events. And perhaps that is why we are where we are with this pandemic. The current event is well within the bounds of catastrophe modeling, or potentially a lot worse if the fatality ratio was in line with that of the SARS outbreak. “When it comes to infectious diseases, we must learn from history. So, if we take SARS, rather than describing it as a unique event, we need to consider all the possible variants that could occur to ensure we are better able to forecast the type of event we are experiencing now.” Within Model Parameters A COVID-19-type event scenario is well within risk model parameters. The RMS® Infectious Diseases Model within its LifeRisks®platform incorporates a range of possible source infections, which includes coronavirus, and the company has been applying model analytics to forecast the potential development tracks of the current outbreak. Launched in 2007, the Infectious Diseases Model was developed in response to the H5N1 virus. This pathogen exhibited a mortality rate of approximately 60 percent, triggering alarm bells across the life insurance sector and sparking demand for a means of modeling its potential portfolio impact. The model was designed to produce outputs specific to mortality and morbidity losses resulting from a major outbreak. In 2006, H5N1 exhibited a mortality rate of approximately 60 percent, triggering alarm bells across the life insurance sector and sparking demand for a means of modeling its potential portfolio impact The probabilistic model is built on two critical pillars. The first is modeling that accurately reflects both the science of infectious disease and the fundamental principles of epidemiology. The second is a software platform that allows firms to address questions based on their exposure and experience data. “It uses pathogen characteristics that include transmissibility and virulence to compartmentalize a pathological epidemiological model and estimate an abated mortality and morbidity rate for the outbreak,” explains Dr. Brice Jabo, medical epidemiologist at RMS. “The next stage is to apply factors including demographics, vaccines and pharmaceutical and non-pharmaceutical interventions to the estimated rate. And finally, we adjust the results to reflect the specific differences in the overall health of the portfolio or the country to generate an accurate estimate of the potential morbidity and mortality losses.” The model currently spans 59 countries, allowing for differences in government strategy, health care systems, vaccine treatment, demographics and population health to be applied to each territory when estimating pandemic morbidity and mortality losses. Breaking Down the Virus In the case of COVID-19, transmissibility — the average number of infections that result from an initial case — has been a critical model parameter. The virus has a relatively high level of transmissibility, with data showing that the average infection rate is in the region of 1.5-3.5 per initial infection. However, while there is general consensus on this figure, establishing an estimate for the virus severity or virulence is more challenging, as Jabo explains: “Understanding the virulence of the disease enables you to assess the potential burden placed on the health care system. In the model, we therefore track the proportion of mild, severe, critical and fatal cases to establish whether the system will be able to cope with the outbreak. However, the challenge factor is that this figure is very dependent on the number of tests that are carried out in the particular country, as well as the eligibility criteria applied to conducting the tests.” An effective way of generating more concrete numbers is to have a closed system, where everyone in a particular environment has a similar chance of contracting the disease and all individuals are tested. In the case of COVID-19 these closed systems have come in the form of cruise ships. In these contained environments, it has been possible to test all parties and track the infection and fatality rates accurately. Another parameter tracked in the model is non-pharmaceutical intervention — those measures introduced in the absence of a vaccine to slow the progression of the disease and prevent health care systems from being overwhelmed. Suppression strategies are currently the most effective form of defense in the case of COVID-19. They are likely to be in place in many countries for a number of months as work continues on a vaccine. “This is an example of a risk that is hugely dependent on government policy for how it develops,” says Woo. “In the case of China, we have seen how the stringent policies they introduced have worked to contain the first wave, as well as the actions taken in South Korea. There has been concerted effort across many parts of Southeast Asia, a region prone to infectious diseases, to carry out extensive testing, chase contacts and implement quarantine procedures, and these have so far proved successful in reducing the spread. The focus is now on other parts of the world such as Europe and the Americas as they implement measures to tackle the outbreak.” The Infectious Diseases Model’s vaccine and pharmaceutical modifiers reflect improvements in vaccine production capacity, manufacturing techniques and the potential impact of antibacterial resistance. While an effective treatment is, at time of writing, still in development, this does allow users to conduct “what-if” scenarios. “Model users can apply vaccine-related assumptions that they feel comfortable with,” Jabo says. “For example, they can predict potential losses based on a vaccine being available within two months that has an 80 percent effectiveness rate, or an antiviral treatment available in one month with a 60 percent rate.” Data Upgrades Various pathogens have different mortality and morbidity distributions. In the case of COVID-19, evidence to date suggests that the highest levels of mortality from the virus occur in the 60-plus age range, with fatality levels declining significantly below this point. However, recent advances in data relating to immunity levels has greatly increased our understanding of the specific age range exposed to a particular virus. “Recent scientific findings from data arising from two major flu viruses, H5N1 and A/H7N9, have had a significant impact on our understanding of vulnerability,” explains Woo. “The studies have revealed that the primary age range of vulnerability to a flu virus is dependent upon the first flu that you were exposed to as a child. “There are two major flu groups to which everyone would have had some level of exposure at some stage in their childhood. That exposure would depend on which flu virus was dominant at the time they were born, influencing their level of immunity and which type of virus they are more susceptible to in the future. This is critical information in understanding virus spread and we have adapted the age profile vulnerability component of our model to reflect this.” Recent model upgrades have also allowed for the application of detailed information on population health, as Jabo explains: “Preexisting conditions can increase the risk of infection and death, as COVID-19 is demonstrating. Our model includes a parameter that accounts for the underlying health of the population at the country, state or portfolio level. “The information to date shows that people with co-morbidities such as hypertension, diabetes and cardiovascular disease are at a higher risk of death from COVID-19. It is possible, based on this data, to apply the distribution of these co-morbidities to a particular geography or portfolio, adjusting the outputs based on where our data shows high levels of these conditions.” Predictive Analytics The RMS Infectious Diseases Model is designed to estimate pandemic loss for a 12-month period. However, to enable users to assess the potential impact of the current pandemic in real time, RMS has developed a hybrid version that combines the model pandemic scenarios with the number of cases reported. “Using the daily cases numbers issued by each country,” says Jabo, “we project forward from that data, while simultaneously projecting backward from the RMS scenarios. Using this hybrid approach, it allows us to provide a time-dependent estimate for COVID-19. In effect, we are creating a holistic alignment of observed data coupled with RMS data to provide our clients with a way to understand how the evolution of the pandemic is progressing in real time.” Aligning the observed data with the model parameters makes the selection of proper model scenarios more plausible. The forward and backward projections, as illustrated, not only allow for short-term projections, but also forms part of model validation and enables users to derive predictive analytics to support their portfolio analysis. “Staying up to date with this dynamic event is vital,” Jabo concludes, “because the impact of the myriad government policies and measures in place will result in different potential scenarios, and that is exactly what we are seeing happening.”

Helen Yates
September 06, 2019
Insurance: The next 10 years

Mohsen Rahnama, Cihan Biyikoglu and Moe Khosravy of RMS look to 2029, consider the changes the (re)insurance industry will have undergone and explain why all roads lead to a platform Over the last 30 years, catastrophe models have become an integral part of the insurance industry for portfolio risk management. During this time, the RMS model suite has evolved and expanded from the initial IRAS model  — which covered California earthquake — to a comprehensive and diverse set of models covering over 100 peril-country combinations all over the world.  RMS Risk Intelligence™, an open and flexible platform, was recently launched, and it was built to enable better risk management and support profitable risk selection. Since the earliest versions of catastrophe models, significant advances have been made in both technology and computing power. These advances allow for a more comprehensive application of new science in risk modeling and make it possible for modelers to address key sources of model and loss uncertainty in a more systematic way.  These and other significant changes over the last decade are shaping the future of insurance. By 2029, the industry will be fully digitized, presenting even more opportunity for disruption in an era of technological advances. In what is likely to remain a highly competitive environment, market participants will need to differentiate based on the power of computing speed and the ability to mine and extract value from data to inform quick, risk-based decisions. Laying the Foundations So how did we get here? Over the past few decades we have witnessed several major natural catastrophes including Hurricanes Andrew, Katrina and Sandy; the Northridge, Kobe, Maule, Tōhoku and Christchurch Earthquakes; and costly hurricanes and California wildfires in 2017 and 2018. Further, human-made catastrophes have included the terrorist attacks of 9/11 and major cyberattacks, such as WannaCry and NotPetya.  Each of these events has changed the landscape of risk assessment, underwriting and portfolio management. Combining the lessons learned from past events, including billions of dollars of loss data, with new technology has enhanced the risk modeling methodology, resulting in more robust models and a more effective way to quantify risk across diverse regions and perils. The sophistication of catastrophe models has increased as technology has enabled a better understanding of root causes and behavior of events, and it has improved analysis of their impact. Technology has also equipped the industry with more sophisticated tools to harness larger datasets and run more computationally intensive analytics. These new models are designed to translate finer-grained data into deeper and more detailed insights. Consequently, we are creating better models while also ensuring model users can make better use of model results through more sophisticated tools and applications.  A Collaborative Approach In the last decade, the pace at which technology has advanced is compelling. Emerging technology has caused the insurance industry to question if it is responding quickly and effectively to take advantage of new opportunities. In today’s digital world, many segments of the industry are leveraging the power and capacity enabled by Cloud-computing environments to conduct intensive data analysis using robust analytics.  Technology has also equipped the industry with more sophisticated tools to harness larger datasets Such an approach empowers the industry by allowing information to be accessed quickly, whenever it is needed, to make effective, fully informed decisions. The development of a standardized, open platform creates smooth workflows and allows for rapid advancement, information sharing and collaboration in growing common applications.   The future of communication between various parties across the insurance value chain — insurers, brokers, reinsurers, supervisors and capital markets — will be vastly different from what it is today. By 2029, we anticipate the transfer of data, use of analytics and other collaborations will be taking place across a common platform. The benefits will include increased efficiency, more accurate data collection and improvements in underwriting workflow. A collaborative platform will also enable more robust and informed risk assessments, portfolio rollout processes and risk transfers. Further, as data is exchanged it will be enriched and augmented using new machine learning and AI techniques. An Elastic Platform We continue to see technology evolve at a very rapid pace. Infrastructure continues to improve as the cost of storage declines and computational speed increases. Across the board, the incremental cost of computing technology has come down.  Software tools have evolved accordingly, with modern big data systems now capable of handling hundreds if not thousands of terabytes of data. Improved programming frameworks allow for more seamless parallel programming. User-interface components reveal data in ways that were not possible in the past. Furthermore, this collection of phenomenal advances is now available in the Cloud, with the added benefit that it is continuously self-improving to support growing commercial demands. In addition to helping avoid built-in obsolescence, the Cloud offers “elasticity.” Elasticity means accessing many machines when you need them and fewer when you don’t. It means storage that can dynamically grow and shrink, and computing capacity that can follow the ebb and flow of demand.  In our world of insurance and data analytics, the macro cycles of renewal seasons and micromodeling demand bursts can both be accommodated through the elastic nature of the Cloud. In an elastic world, the actual cost of supercomputing goes down, and we can confidently guarantee fast response times.  Empowering Underwriters A decade from now, the industry will look very different, not least due to changes within the workforce and the risk landscape. First-movers and fast-followers will be in a position of competitive advantage come 2029 in an industry where large incumbents are already partnering with more agile “insurtech” startups.  The role of the intermediary will continue to evolve, and at every stage of risk transfer — from insured to primary insurer, reinsurer and into the capital markets — data sharing and standardization will become key success factors. Over the next 10 years, as data becomes more standardized and more widely shared, the concept of blockchain, or distributed ledger technology, will move closer to becoming a reality.  This standardization, collaboration and use of advanced analytics are essential to the future of the industry. Machine learning and AI, highly sophisticated models and enhanced computational power will enable underwriters to improve their risk selection and make quick, highly informed decisions.  And this ability will enhance the role of the insurance industry in society, in a changing and altogether riskier world. The tremendous protection gap can only be tackled when there is more detailed insight and differentiation around each individual risk. When there is greater insight into the underlying risk, there is less need for conservatism, risks become more accurately and competitively priced, and (re)insurers are able to innovate to provide products and solutions for new and emerging exposures.  Over the coming decade, models will require advanced computing technology to fully harness the power of big data. Underwater robots are now probing previously unmapped ocean waters to detect changes in temperatures, currents, sea level and coastal flooding. Drones are surveying our built-up environment in fine detail. Artificial intelligence and machine learning algorithms are searching for patterns of climate change in these new datasets, and climate models are reconstructing the past and predicting the future at a resolution never before possible. These emerging technologies and datasets will help meet our industry’s insatiable demand for more robust risk assessment at the level of an individual asset. This explosion of data will fundamentally change the way we think about model execution and development, as well as the end-to-end software infrastructure. Platforms will need to be dynamic and forward-looking verses static and historic in the way they acquire, train, and execute on data. The industry has already transformed considerably over the past five years, despite traditionally being considered a laggard in terms of its technology adoption. The foundation is firmly in place for a further shift over the next decade where all roads are leading to a common, collaborative industry platform, where participants are willing to share data and insights and, as they do so, open up new markets and opportunities.  RMS Risk Intelligence The analytical and computational power of the Risk Intelligence (RI) platform enables the RMS model development team to bring the latest science and research to the RMS catastrophe peril model suite and build the next generation of high-definition models. The functionality and high performance of RI allows the RMS team to assess elements of model and loss uncertainty in a more robust way than before.  The framework of RI is flexible, modular and scalable, allowing the rapid integration of future knowledge with a swifter implementation and update cycle. The open modeling platform allows model users to extract more value from their claims experience to develop vulnerability functions that represent a view of risk specific to their data or to use custom-built alternatives. This enables users to perform a wide range of sensitivity tests and take ownership of their view of risk. Mohsen Rahnama is chief risk modeling officer and executive vice president, models and data, Cihan Biyikoglu is executive vice president, product and Moe Khosravy is executive vice president, software and platform at RMS

NIGEL ALLEN
September 06, 2019
A Need for Multi-Gap Analysis

The insurance protection gap is composed of emerging markets and high-risk and intangible exposures There cannot be many industries that recognize that approximately 70 percent of market potential is untapped. Yet that is the scale of opportunity in the expanding “protection gap”. Power outage in lower Manhattan, New York, after Hurricane Sandy While efforts are ongoing to plug the colossal shortage, any meaningful industry foray into this barren range must acknowledge that the gap is actually multiple gaps, believes Robert Muir-Wood, chief research officer at RMS.  “It is composed of three distinct insurance gaps — high risk, emerging markets and intangibles — each with separate causes and distinct solutions. Treating it as one single challenge means we will never achieve the loss clarity to tackle the multiple underlying issues.” High-risk, high-value gaps exist in regions where potential loss magnitude outweighs the ability of the industry to refund post-catastrophe. High deductibles and exclusions reduce coverage appeal and stunt market growth. “Take California earthquake. The California Earthquake Authority (CEA) was launched in 1996 to tackle the coverage dilemma exposed by the Northridge disaster. Yet increased deductibles and new exclusions led to a 30 percent gap expansion. And while recent changes have seen purchase uptick, penetration is around 12-14 percent for California homeowners.” On the emerging market front, micro- and meso-insurance and sovereign risk transfer efforts to bridge the gap have achieved limited success. “The shortfall in emerging economies remains static at between 80 to 100 percent,” he states, “and it is not just a developing world issue, it’s clearly evident in mature markets like Italy.” “The protection gap is composed of three distinct insurance gaps — high risk, emerging markets and intangibles — each with separate causes and distinct solutions” Robert Muir-Wood RMS A further fast-expanding gap is intangible assets. “In 1975, physical assets accounted for 83 percent of the value of S&P 500 companies,” Muir-Wood points out. “By 2015, that figure was 16 percent, with 84 percent composed of intangible assets such as IP, client data, brand value and innovation potential.”  While non-damage business interruption cover is evolving, expanding client demand for events such as power outage, cloud disruption and cyberbreach greatly outpace delivery. To start closing these gaps, Muir-Wood believes protection gap analytics are essential. “We have to first establish a consistent measurement for the difference between insured and total loss and split out ‘penetration’ and ‘coverage’ gaps. That gives us our baseline from which to set appropriate targets and monitor progress. “Probabilistic cat risk models will play a central role, particularly for the high-risk protection gap, where multiple region and peril-specific models already exist. However, for intangibles and emerging markets, where such models have yet to gain a strong foothold, focusing on scenario events might prove a more effective approach.” Variations in the gaps according to severity and geography of the catastrophe could be expressed in the form of an exceedance probability curve, showing how the percentage of uninsured risk varies by return period. “There should be standardization in measuring and reporting the gap,” he concludes. “This should include analyzing insured and economic loss based on probabilistic models, separating the effects of the penetration and coverage gaps, and identifying how gaps vary with annual probability and location.” 

Helen Yates
September 06, 2019
Severe Convective Storms: A New Peak Peril?

Severe convective storms (SCS) have driven U.S. insured catastrophe losses in recent years with both attritional and major single-event claims now rivaling an average hurricane season. EXPOSURE looks at why SCS losses are rising and asks how (re)insurers should be responding At the time of writing, 2019 was already shaping up to be another active season for U.S. severe convective storms (SCS), with at least eight tornadoes daily over a period of 12 consecutive days in May. It was the most May tornadoes since 2015, with no fewer than seven outbreaks of SCS across central and eastern parts of the U.S. According to data from the National Oceanic and Atmospheric Administration (NOAA), there were 555 preliminary tornado reports, more than double the average of 276 for the month in the period of 1991-2010. According to the current numbers, May 2019 produced the second-highest number of reported tornadoes for any month on record after April 2011, which broke multiple records in relation to SCS and tornado touchdowns. It continues a trend set over the past two decades, which has seen SCS losses increasing significantly and steadily. In 2018, losses amounted to US$18.8 billion, of which US$14.1 billion was insured. This compares to insurance losses of US$15.6 billion for hurricane losses in the same period. While losses from SCS are often the buildup of losses from multiple events, there are examples of single events costing insurers and reinsurers over US$3 billion in claims. This includes the costliest SCS to date, which hit Tuscaloosa, Alabama, in April 2011, involving several tornado touchdowns and causing US$7.9 billion in insured damage. The second-most-costly SCS occurred in May of the same year, striking Joplin, Missouri, and other locations, resulting in insured losses of nearly US$7.6 billion. “The trend in the scientific discussion is that there might be fewer but more-severe events” Juergen Grieser RMS According to RMS models, average losses from SCS now exceed US$15 billion annually and are in the same range as hurricane average annual loss (AAL), which is also backed up by independently published scientific research. “The losses in 2011 and 2012 were real eye-openers,” says Rajkiran Vojjala, vice president of modeling at RMS. “SCS is no longer a peril with events that cost a few hundred million dollars. You could have cat losses of US$10 billion in today’s money if there were events similar to those in April 2011.”  Nearly a third of all average annual reported tornadoes occur in the states of Texas, Oklahoma, Kansas and Nebraska, all states that are within the “Tornado Alley.” This is where cold, dry polar air meets warm, moist air moving up from the Gulf of Mexico, causing strong convective activity. “A typical SCS swath affects many states. So the extent is large, unlike, say, wildfire, which is truly localized to a small particular region,” says Vojjala. Research suggests the annual number of Enhanced Fujita (EF) scale EF2 and stronger tornadoes hitting the U.S. has trended upward over the past 20 years; however, there is some doubt over whether this is a real meteorological trend. One explanation could be that increased observational practices simply mean that such weather phenomena are more likely to be recorded, particularly in less populated regions.  According to Juergen Grieser, senior director of modeling at RMS, there is a debate whether part of the increase in claims relating to SCS could be attributed to climate change. “A warmer climate means a weaker jet stream, which should lead to less organized convection while the energy of convection might increase,” he says. “The trend in the scientific discussion is that there might be fewer but more-severe events.” Claims severity rather than claims frequency is a more significant driver of losses relating to hail events, he adds. “We have an increase in hail losses of about 11 percent per year over the last 15 years, which is quite a lot. But 7.5 percent of that is from an increase in the cost of individual claims,” explains Grieser. “So, while the claims frequency has also increased in this period, the individual claim is more expensive now than it was ever before.”  Claims go ‘Through the Roof’ Another big driver of loss is likely to be aging roofs and the increasing exposure at risk of SCS. The contribution of roof age was explored in a blog last year by Stephen Cusack, director of model development at RMS. He noted that one of the biggest changes in residential exposure to SCS over the past two decades has been the rise in the median age of housing from 30 years in 2001 to 37 years in 2013. A changing insurance industry climate is also a driver for increased losses, thinks Vojjala. “There has been a change in public perception on claiming whereby even cosmetic damage to roofs is now being claimed and contractors are chasing hailstorms to see what damage might have been caused,” he says. “So, there is more awareness and that has led to higher losses. “The insurance products for hail and tornado have grown and so those perils are being insured more, and there are different types of coverage,” he notes. “Most insurers now offer not replacement cost but only the actual value of the roofs to alleviate some of the rising cost of claims. On the flip side, if they do continue offering full replacement coverage and a hurricane hits in some of those areas, you now have better roofs.” How insurance companies approach the peril is changing as a result of rising claims. “Historically, insurance and reinsurance clients have viewed SCS as an attritional loss, but in the last five to 10 years the changing trends have altered that perception,” says Vojjala. “That’s where there is this need for high-resolution modeling, which increasingly our clients have been asking for to improve their exposure management practices. “With SCS also having catastrophic losses, it has stoked interest from the ILS community as well, who are also experimenting with parametric triggers for SCS,” he adds. “We usually see this on the earthquake or hurricane side, but increasingly we are seeing it with SCS as well.” 

Helen Yates
September 06, 2019
Risk in 2030

At this year’s RMS Exceedance conference in Miami, Robert Muir-Wood and Michael Steel imagined 10 future risks

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