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

Today’s stress test for tomorrow’s climate

Why the PRA’s stress test has pushed climate change to the top of (re)insurance company agendas

As part of its biennial insurance stress test, the U.K. insurance industry regulator has — for the first time  — asked insurers and reinsurers to conduct an exploratory exercise in relation to climate change. Using predictions published by the United Nations’ Intergovernmental Panel on Climate Change (IPCC) and in other academic literature, the Bank of England’s Prudential Regulation Authority (PRA) has come up with a series of future climate change scenarios, which it has asked (re)insurers to use as a basis for stress-testing the impact on their assets and liabilities.

The PRA stress test comes at a time when pressure is building for commercial and financial services businesses around the world to assess the likely impact of climate change on their business, through initiatives such as the Task Force for Climate-Related Financial Disclosures (TCFD). Submission deadline for the stress-tested scenarios is October 31, 2019, following which the PRA will publish a summary of overall results.

From a property catastrophe (re)insurance industry perspective, the importance of assessing the potential impact, both in the near and long term, is clear. Companies must ensure their underwriting strategies and solvency levels are adequate so as to be able to account for additional losses from rising sea levels, more climate extremes, and potentially more frequent and/or intense natural catastrophes. Then there’s the more strategic considerations in the long term — how much coverages change and what will consumers demand in a changing climate?

The PRA stress test, explains Callum Higgins, product manager of global climate at RMS, is the regulator’s attempt to test the waters. The hypothetical narratives are designed to help companies think about how different plausible futures could impact their business models, according to the PRA. “The climate change scenarios are not designed to assess current financial resilience but rather to provide additional impetus in this area, with results comparable across firms to better understand the different approaches companies are using.”

“There is pressure on clients to respond to this because those that don’t participate will probably come under greater scrutiny” — Callum Higgins, RMS

RMS is particularly well placed to support (re)insurers in responding to the “Assumptions to Assess the Impact on an Insurer’s Liabilities” section of the climate change scenarios, with catastrophe models the perfect tools to evaluate such physical climate change risk to liabilities. This portion of the stress test examines how changes in both U.S. hurricane and U.K. weather risk under the different climate change scenarios may affect losses.

The assumptions around U.K. weather include shifts in U.K. inland and coastal flood hazard, looking at the potential loss changes from increased surface runoff and sea level rise. While in the U.S., the assumptions include a 10 percent and 20 percent increase in the frequency of major hurricanes by 2050 and 2100, respectively. 

“While the assumptions and scenarios are hypothetical, it is important (re)insurers use this work to develop their capabilities to understand physical climate change risk,” says Higgins. “At the moment, it is exploratory work, but results will be used to guide future exercises that may put (re)insurers under pressure to provide more sophisticated responses.”

Given the short timescales involved, RMS has promptly modified the necessary models in time for clients to benefit for their submissions. “To help clients start thinking about how to respond to the PRA request, we have provided them with industrywide factors, which allow for the approximation of losses under the PRA assumptions but will likely not accurately reflect the impact on their portfolios. For this reason, we are also running (re)insurers’ own exposures through the adjusted models, via RMS Analytical Services, better satisfying the PRA’s requirements for those who choose this approach.

“To reasonably represent these assumptions and scenarios, we think it does need help from vendor companies like RMS to adjust the model data appropriately, which is possibly out of scope for many businesses,” he adds.

Detailed results based on the outcome of the stress-test exercise can be applied to use cases beyond the regulatory submission for the PRA. These or other similar scenarios can be used to sensitivity test possible answers to questions such as how will technical pricing of U.K. flood be affected by climate change, how should U.S. underwriting strategy shift in response to sea level rise or how will capital adequacy requirements change as a result of climate change — and inform strategic decisions accordingly.

A data step change

With the introduction of the Risk Data Open Standard, the potential now exists to change the way the (re)insurance industry interacts with risk modeling data

In May, RMS introduced the (re)insurance industry to a new open data standard. Set to redefine how the market structures data, the Risk Data Open Standard (RDOS) offers a flexible, fully transparent and highly efficient framework — spanning all risks, models and contracts and information sets — that can be implemented using a wide range of data technology.

“The RDOS has been constructed to hold the entire set of information that supports the analysis of any risk” — Ryan Ogaard, RMS

That this new standard has the potential to alter fundamentally how the market interacts with exposure data is not hyperbole. Consider the formats that it is replacing. The RMS Exposure and Results Data Modules (EDM and RDM) have been the data cornerstones of the property catastrophe market for over 20 years. Other vendors use similar data formats, and some catastrophe modeling firms have their own versions. These information workhorses have served the sector well, transforming the way property catastrophe risk is transacted, priced and managed.

Out with the old

But after over two decades of dedicated service, it is past time these formats were put out to pasture. Built to handle a narrow range of modeling approaches, limited in their ability to handle multiple information formats, property-centric by design  and powered by outdated technology, the EDM/RDM and other formats represent “old-gen” standards crumbling under current data demands.

“EDM and RDM have earned their status as the de facto standards for property catastrophe data exchange,” explains Ryan Ogaard, senior vice president at RMS. “Clearly documented, easy to implement, SQL-based, they were groundbreaking and have been used extensively in systems and processes for over 20 years. But the industry has evolved well beyond the capabilities of all the existing formats, and a new data model must be introduced to facilitate innovation and efficiency across our industry.”

The RDOS is not the only attempt to solve the data formatting challenge. Multiple other initiatives have been attempted, or are underway, to improve data efficiency within the insurance industry. However, Ogaard believes all of these share one fatal flaw — they do not go far enough.

“I have been involved in various industry groups exploring ways to overcome data challenges,” he explains, “and have examined the potential of different options. But in every instance, what is clear is that they would not advance the industry far enough to make them worth switching to.”

The switching costs are a major issue with any new data standard. Transitioning to a new format from one so firmly embedded within your data hierarchy is a considerable move. To shift to a new standard that offers only marginal relief from the data pains of the current system would not be enough.

“The industry needs a data container that can be extended to new coverages, risk types or contracts,” he states. “If we require a different format for every line of business or type of model, we end up with a multiplicative world of data inefficiency. Look at cyber risk. We’ve already created a separate new standard for that information. If our industry is truly going to move forward, the switch must solve our challenges in the short, medium and long term. That means a future-proof design to handle new models, risks and contracts — ideally all in one container.”

Setting the standard

Several years in the making, the RDOS is designed to address every deficiency in the current formatting framework, providing a data container that can be easily modified as needs change and can deliver information in a single, auditable format that supports a wide range of analytics.

“The RDOS is designed to be extended across several dimensions,” Ogaard continues. “It can handle the data and output to support any modeling algorithm — so RMS, or anyone else, can use it as a basis for new or existing models. It was originally built to support our high-definition (HD) modeling, which requires a domain-specific language to represent policy or treaty terms and structures — that was not possible with the old format. During that process, we realized that we should design a container that would not have to be replaced in the future when we inevitably build other types of models.”

The RDOS can also span all business lines. It is designed to accommodate the description of any risk item or subject at risk. The standard has inherent flexibility — new tables can be introduced to the framework without disrupting existing sets, while current tables can be extended to handle information for multiple model types or additional proprietary data.

“EDM and RDM were fundamental to creating a much more stable, resilient and dynamic marketplace,” says Ogaard. “That level of modeling simply isn’t available across other lines — but with the RDOS it can be. Right off the bat, that has huge implications for issues such as clash risk. By taking the data that exists across your policy and treaty systems and converting it into a single data format, you can then apply an accumulation engine to evaluate all clash scenarios. So, essentially, you can tackle accumulation risk across all business lines.”

It is also built to encompass the full “risk story.” Current data formats essentially provide exposure and modeling results, but lack critical information on how the exposure was used to create the results. This means that anyone receiving these data sets must rely on an explanation of how an analysis was done — or figure it out themselves.

“The RDOS has been constructed to hold the entire set of information that supports the analysis of any risk,” he explains. “This includes exposures,
(re)insurance coverage information, the business structure used to create the results, complete model settings and adjustments, the results, and the linkage between the information.  Multiple analyses can also be included in a single container. That means more time can be spent on accurate risk decision-making.”

The RDOS is also independent of any specific technology and can be implemented in modern object relational technology, making it highly flexible. It can also be implemented in SQL Server if the limitations of a relational representation are adequate for the intended usage. The insurance industry, and cat analytics software, has been slow to adopt the power of tools such as Parquet, Spark, Athena and other new and powerful (and often open-source) data tools that can drive more data insights.

Opening the box

For the RDOS to achieve its full potential, however, it cannot be constrained by ownership. By its very nature, it must be an open standard operated in a neutral environment if it is to be adopted by all and serve a larger market purpose.

RMS recognized this and donated the RDOS to the industry (and beyond) as an open standard, harnessing open-source principles common in the software industry. Taking this route is perhaps not surprising given the executive leadership now in place at the company, with both CEO Karen White and Executive Vice President of Product Cihan Biyikoglu having strong open-source credentials.

“When they saw the RDOS,” Ogaard explains, “it clearly had all of the hallmarks of an open-source candidate. It was being built by a leading market player with an industrywide purpose that required a collaborative approach.”

What RMS has created with the RDOS represents a viable standard — but rather than a finished product, it is a series of building blocks designed to create a vast range of new applications from across the market. And to do that it must be a completely open standard that can evolve with the industry.

“Some companies claim to have open standards,” he continues, “but by that they mean that you can look inside the box. Truly open standards are set up to be overseen and actually modified by the industry. With the RDOS, companies can not only open the box, but take the standard out, use it and modify it to create something better. They can build additions and submit them for inclusion and use by the entire industry. The RDOS will not be driven by RMS needs and priorities — it will exist as a separate entity. RMS cannot build every potential solution or model. We hope that by making this an open standard, new synergy is created that will benefit everyone — including us, of course.”

Under scrutiny

To create a standard fit for all, RMS accepted that the RDOS could not be built in isolation and pushed out into the market — it had to be tested, the underlying premise reviewed, the format scrutinized.

To ensure this, the company set up a steering committee from across the (re)insurance market. Charged with putting the RDOS through its paces, the committee members are given a central role in virtually every development stage.  The committee is currently fourteen companies strong and growing.  It will be dynamic and membership will change over time as issues and company focuses evolve. The membership list can be seen at

“You cannot sit in an ivory tower and decide what might work for the industry as a whole,” Ogaard explains. “You need a robust vetting process and by creating this group of leading (re)insurance practitioners, each committed not simply to the success of the project but to the development of the best possible data solution, the RDOS  will be guided by the industry, not just one company.”

The role of the committee is twofold. Currently, it is to review the existing specification, documentation and tooling to determine if they are ready for market consumption. Once the RDOS is published, the committee’s role will be to advise on the priorities and scope of future developments based on market-led requests for change and improvement.

Set for its industry launch in January 2020, the data specification, documentation and tooling is currently undergoing an end-to-end review. While not yet released publicly, it is already used within the framework of the recently launched risk management platform RMS Risk Intelligence™.

“Almost every open standard in any industry is based on a real, working product — not a theoretical construct,” he states. “Because the RDOS was built for a practical purpose and is in real-world use, it is much more likely to hold up to wider use and scrutiny.”

So, while the RDOS may be an unknown entity to the wider market, it has already established its data credentials within the RMS model framework.

Of course, there remains the fundamental challenge of shifting from one data format to another — but measures are already in place to make this as painless as possible.

“The RDOS is essentially a superset of the original EDM and RDM formats,” he explains, “offering an environment in which the new and old standards are interchangeable. So, a company can translate an EDM into an RDOS and vice versa. The open standard tooling will include translators to make this translation. The user will therefore be able to operate both formats simultaneously and, as they recognize the RDOS data benefits, transition to that environment at their own pace. The RDOS could be extended to include other modelers’ data fields as well — so could solve model interoperability issues — if the industry decides to use it this way.”

The standard will launch on the global development platform GitHub, which supports open-source standards, offering a series of downloadable assets including the RDOS specification, documentation, tools and data so that companies can create their own implementation and translate to and from old data formats.

The potential that it creates is considerable and to a degree only limited by the willingness of users to push boundaries. 

“Success could come in several forms,” Ogaard concludes. “The RDOS becomes the single universal container for data exchange, creating huge efficiencies. Or it creates a robust ecosystem of developers opening up new opportunities and promoting greater industry choice. Or it supports new products that could not be foreseen today and creates synergies that drive more value — perhaps even outside the traditional market. Ideally, all of these things.”

Shaking up workers’ compensation

Are (re)insurers sufficiently capitalized to withstand a major earthquake in a metropolitan area during peak hours? 

The U.S. workers’ compensation insurance market continues to generate underwriting profit. According to Fitch Ratings, 2019 is on track to mark the fifth consecutive year of profits and deliver a statutory combined ratio of 86 percent in 2018. Since 2015, it has achieved an annual average combined ratio of 93 percent.

The market’s size has increased considerably since the 2008 financial crisis sparked a flurry of activity in the workers’ compensation arena. Over the last 10 years, written premiums have risen 50 percent from approximately US$40 billion to almost US$60 billion, aided by low unemployment and growth in rate and wages. 

Yet market conditions are changing. The pricing environment is deteriorating, prior-year reserve releases are slowing and severity is ticking upwards. And while loss reserves currently top US$150 billion, questions remain over whether these are sufficient to bear the brunt of a major earthquake in a highly populated area.

The big one

California represents over 20 percent of the U.S. workers’ compensation market. The Workers’ Compensation Insurance Rating Bureau of California (WCIRB) forecasts a written premium pot of US$15.7 billion for 2019, a slight decline on 2018’s US$17 billion figure. 

“So, the workers’ compensation sector’s largest premium is concentrated in the area of the U.S. most exposed to earthquake risk,” explains Nilesh Shome, vice president at RMS. “This problem is unique to the U.S., since in most other countries occupational injury is covered by government insurance schemes instead of the private market. Further, workers’ compensation policies have no limits, so they can be severely impacted by a large earthquake.”

Workers’ compensation insurers enjoy relatively healthy balance sheets, with adequate profitability and conservative premium-to-surplus ratios. But, when you assess the industry’s exposure to large earthquakes in more detail, the surplus base starts to look a little smaller.

“We are also talking about a marketplace untested in modern times,” he continues. “The 1994 Northridge Earthquake in Los Angeles, for example, while causing major loss, occurred at 4:30 a.m. when most people were still in bed, so had limited impact from a workers’ compensation perspective.”

Analyzing the numbers

Working with the WCIRB, RMS modeled earthquake scenarios using Version 17 of the RMS® North America Earthquake Casualty Model, which incorporates the latest science in earthquake hazard and vulnerability research. The portfolio provided by the WCIRB contained exposure information for 11 million full-time-equivalent employees, including occupation details for each.

The analysis showed that the average annual estimated insured loss is US$29 million, which corresponds to 0.5 cents per $100 payroll and $2.50 per employee.

The 1-in-100-year insurance loss is expected to exceed US$300 million, around 5,000 casualties including 300 fatalities; while at peak work-time hours, the loss could rise to US$1.5 billion. For a 1-in-250-year loss, the figure could top US$1.4 billion and more than 1,000 fatalities, rising to US$5 billion at peak work-time hours.  But looking at the magnitude 7.8 San Francisco Earthquake in 1906 at 5:12 a.m., the figure would be 7,300 injuries, 1,900 fatalities and around US$1 billion in loss. At peak work hours, this would rise to 22,000 casualties, 5,800 fatalities and a US$3 billion loss.

To help reduce the impact of major earthquakes, RMS is working with the Berkeley Research Lab and the United States Geological Survey (USGS) to research the benefits of an earthquake early warning system (EEWS) and safety measures such as drop-cover-hold and evacuating buildings after an EEWS alarm. Initial studies indicate that an EEWS alert for the large, faraway earthquakes such as the 1857 magnitude 7.9 Fort Tejon Earthquake near Los Angeles can reduce injuries by 20 percent-50 percent. 

Shome concludes: “It is well known in the industry that workers’ compensation loss distribution has a long tail, and at conferences RMS has demonstrated how our modeling best captures this tail. The model considers many low probability, high consequence events by accurately modeling the latest USGS findings.”

Like moths to the flame

Why is it that, in many different situations and perils, people appear to want to relocate toward the risk? What is the role of the private insurance and reinsurance industry in curbing their clients’ risk tropism? 

Florida showed rapid percentage growth in terms of exposure and number of policyholders

If the Great Miami Hurricane of 1926 were to occur again today it would result in insurance losses approaching US$200 billion. Even adjusted for inflation, that is hundreds of times more than the US$100 million damage toll in 1926. Over the past 100 years, the Florida coast has developed exponentially, with wealthy individuals drawn to buying lavish coastal properties — and the accompanying wind and storm-surge risks. Since 2000, the number of people living in coastal areas of Florida increased by 4.2 million, or 27 percent, to 19.8 million in 2015, according to the U.S. Census Bureau.

This is an example of unintended “risk tropism,” explains  Robert Muir-Wood, chief research officer at RMS. Just as the sunflower is a ‘heliotrope’, turning toward the sun, research has shown how humans have an innate drive to live near water, on a river or at the beach, often at increased risk of flood hazards.  

“There is a very strong human desire to find the perfect primal location for your house. It is something that is built deeply into the human psyche,” Muir-Wood explains. “People want to live with the sound of the sea, or in the forest ‘close to nature,’ and they are drawn to these locations thinking about all the positives and amenity values, but not really understanding or evaluating the accompanying risk factors.

“People will pay a lot to live right next to the ocean,” he adds. “It’s an incredibly powerful force and they will invest in doing that, so the price of land goes up by a factor of two or three times when you get close to the beach.” 

Even when beachfront properties are wiped out in hurricane catastrophes, far from driving individuals away from a high-risk zone, research shows they simply “build back bigger,” says Muir-Wood. “The disaster can provide the opportunity to start again, and wealthier people move in and take the opportunity to rebuild grander houses. At least the new houses are more likely to be built to code, so maybe the reduction in vulnerability partly offsets the increased exposure at risk.”

Risk tropism can also be found with the encroachment of high-value properties into the wildlands of California, leading to a big increase in wildfire insurance losses. Living close to trees can be good for mental health until those same trees bring a conflagration. Insurance losses due to wildfire exceeded US$10 billion in 2017 and have already breached US$12 billion for last year’s Camp, Hill and Woolsey Fires, according to the California Department of Insurance. It is not the number of fires that have increased, but the number of houses consumed by the fires. 

“Insurance tends to stop working when you have levels of risk above one percent [...] People are unprepared to pay for it” — Robert Muir-Wood, RMS

Muir-Wood notes that the footprint of the 2017 Tubbs Fire, with claims reaching to nearly US$10 billion, was very similar to the area burned during the Hanley Fire of 1964. The principal difference in outcome is driven by how much housing has been developed in the path of the fire. “If a fire like that arrives twice in one hundred years to destroy your house, then the amount you are going to have to pay in insurance premium is going to be more than 2 percent of the value per year,” he says. 

“People will think that’s unjustified and will resist it, but actually insurance tends to stop working when you have levels of risk cost above 1 percent of the property value, meaning, quite simply, that people are unprepared to pay for it.”  

Risk tropism can also be found in the business sector, in the way that technology companies have clustered in Silicon Valley: a tectonic rift within a fast-moving tectonic plate boundary. The tectonics have created the San Francisco Bay and modulate the climate to bring natural air-conditioning.

“Why is it that, around the world, the technology sector has picked locations  — including Silicon Valley, Seattle, Japan and Taiwan — that are on plate boundaries and are earthquake prone?” asks Muir-Wood. “There seems to be some ideal mix of mountains and water. The Bay Area is a very attractive environment, which has brought the best students to the universities and has helped companies attract some of the smartest people to come and live and work in Silicon Valley,” he continues. “But one day there will be a magnitude 7+ earthquake in the Bay Area that will bring incredible disruption, that will affect the technology firms themselves.”

Insurance and reinsurance companies have an important role to play in informing and dissuading organizations and high net worth individuals from being drawn toward highly exposed locations; they can help by pricing the risk correctly and maintaining underwriting discipline. The difficulty comes when politics and insurance collide. 

The growth of Fair Access to Insurance Requirements (FAIR) plans and beach plans, offering more affordable insurance in parts of the U.S. that are highly exposed to wind and quake perils, is one example of how this function is undermined. At its peak, the size of the residual market in hurricane-exposed states was US$885 billion, according to the Insurance Information Institute (III). It has steadily been reduced, partly as a result of the influx of non-traditional capacity from the ILS market and competitive pricing in the general reinsurance market. 

However, in many cases the markets-of-last-resort remain some of the largest property insurers in coastal states. Between 2005 and 2009 (following Hurricanes Charley, Frances, Ivan and Jeanne in 2004), the plans in Mississippi, Texas and Florida showed rapid percentage growth in terms of exposure and number of policyholders. A factor fueling this growth, according to the III, was the rise in coastal properties. 

As long as state-backed insurers are willing to subsidize the cost of cover for those choosing to locate in the riskiest locations, private (re)insurance will fail as an effective check on risk tropism, thinks Muir-Wood. “In California there are quite a few properties that have not been able to get standard fire insurance,” he observes. “But there are state or government-backed schemes available, and they are being used by people whose wildfire risk is considered to be too high.”

Risk in 2030

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


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

2019 is 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 model development 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.” 

Ridgecrest: A wake-up call

Marleen Nyst and Nilesh Shome of RMS explore some of the lessons and implications from the recent sequence of earthquakes in California

On the morning of July 4, the small town of Ridgecrest in California’s Mojave Desert unexpectedly found itself at the center of a major news story after a magnitude 6.4 earthquake occurred close by. This earthquake later transpired to be a foreshock for a magnitude 7.1 earthquake the following day, the strongest earthquake to hit the state for 20 years.

These events, part of a series of earthquakes and aftershocks that were felt by millions of people across the state, briefly reignited awareness of the threat posed by earthquakes in California. Fortunately, damage from the Ridgecrest earthquake sequence was relatively limited. With the event not causing a widespread social or economic impact, its passage through the news agenda was relatively swift. 

But there are several reasons why an event such as the Ridgecrest earthquake sequence should be a focus of attention both for the insurance industry and the residents and local authorities in California. 

“If Ridgecrest had happened in a more densely populated area, this state would be facing a far different economic future than it is today” — Glenn Pomeroy, California Earthquake Authority

“We don’t want to minimize the experiences of those whose homes or property were damaged or who were injured when these two powerful earthquakes struck, because for them these earthquakes will have a lasting impact, and they face some difficult days ahead,” explains Glenn Pomeroy, chief executive of the California Earthquake Authority. 

“However, if this series of earthquakes had happened in a more densely populated area or an area with thousands of very old, vulnerable homes, such as Los Angeles or the San Francisco Bay Area, this state would be facing a far different economic future than it is today — potentially a massive financial crisis,” Pomeroy says. 

Although one of the most populous U.S. states, California’s population is mostly concentrated in metropolitan areas. A major earthquake in one of these areas could have repercussions for both the domestic and international economy. 

Low probability, high impact

Earthquake is a low probability, high impact peril. In California, earthquake risk awareness is low, both within the general public and many (re)insurers. The peril has not caused a major insured loss for 25 years, the last being the magnitude 6.7 Northridge earthquake in 1994.

California earthquake has the potential to cause large-scale insured and economic damage. A repeat of the Northridge event would likely cost the insurance industry today around US$30 billion, according to the latest version of the RMS® North America Earthquake Models, and North-ridge is far from a worst-case scenario. 

From an insurance perspective, one of the most significant earthquake events on record would be the magnitude 9.0 Tōhoku Earthquake and Tsunami in 2011. For California, the 1906 magnitude 7.8 San Francisco earthquake, when Lloyd’s underwriter Cuthbert Heath famously instructed his San Franciscan agent to “pay all of our policyholders in full, irrespective of the terms of their policies”, remains historically significant.

Heath’s actions led to a Lloyd’s payout of around US$50 million at the time and helped cement Lloyd’s reputation in the U.S. market. RMS models suggest a repeat of this event today could cost the insurance industry around US$50 billion. 

But the economic cost of such an event could be around six times the insurance bill — as much as US$300 billion — even before considering damage to infrastructure and government buildings, due to the surprisingly low penetration of earthquake insurance in the state.

Events such as the 1906 earthquake and even Northridge are too far in the past to remain in public consciousness. And the lack of awareness of the peril’s damage potential is demonstrated by the low take-up of earthquake insurance in the state. 

“Because large, damaging earthquakes don’t happen very frequently, and we never know when they will happen, for many people it’s out of sight, out of mind. They simply think it won’t happen to them,” Pomeroy says.

Across California, an average of just 12 percent to 14 percent of homeowners have earthquake insurance. Take-up varies across the state, with some high-risk regions, such as the San Francisco Bay Area, experiencing take-up below the state average. Take-up tends to be slightly higher in Southern California and is around 20 percent in Los Angeles and Orange counties. 

Take-up will typically increase in the aftermath of an event as public awareness rises but will rapidly fall as the risk fades from memory. As with any low probability, high impact event, there is a danger the public will not be well prepared when a major event strikes. 

The insurance industry can take steps to address this challenge, particularly through working to increase awareness of earthquake risk and actively promoting the importance of having insurance coverage for faster recovery. RMS and its insurance partners have also been working to improve society’s resilience against risks such as earthquake, through initiatives such as the 100 Resilient Cities program. 

Understanding the risk

While the tools to model and understand earthquake risk are improving all the time, there remain several unknowns which underwriters should be aware of. One of the reasons the Ridgecrest Earthquake came as such a surprise was that the fault on which it occurred was not one that seismologists knew existed. 

Several other recent earthquakes — such as the 2014 Napa event, the Landers and Big Bear Earthquakes in 1992, and the Loma Prieta Earthquake in 1989 — took place on previously unknown or thought to be inactive faults or fault strands. As well as not having a full picture of where the faults may lie, scientific understanding of how multifaults can link together to form a larger event is also changing. 

Events such as the Kaikoura Earthquake in New Zealand in 2016 and the Baja California Earthquake in Mexico in 2010 have helped inform new scientific thinking that faults can link together causing more damaging, larger magnitude earthquakes. The RMS North America Earthquake Models have also evolved to factor in this thinking and have captured multifault ruptures in the model based on the latest research results. In addition, studying the interaction between the faults that ruptured in the Ridgecrest events will allow RMS to improve the fault connectivity in the models. 

A further learning from New Zealand came via the 2011 Christchurch Earthquake, which demonstrated how liquefaction of soil can be a significant loss driver due to soil condition in certain areas. The San Francisco Bay Area, an important national and international economic hub, could suffer a similar impact in the event of a major earthquake. Across the area, there has been significant residential and commercial development on artificial landfill areas over the last 100 years, which are prone to have significant liquefaction damage, similar to what was observed in Christchurch.

Location, location, location

Clearly, the location of the earthquake is critical to the scale of damage and insured and economic impact from an event. Ridgecrest is situated roughly 200 kilometers north of Los Angeles. Had the recent earthquake sequence occurred beneath Los Angeles instead, then it is plausible that the insured cost could have been in excess of US$100 billion. 

The Puente Hills Fault, which sits underneath downtown LA, wasn’t discovered until around the turn of the century. A magnitude 6.8 Puente Hills event could cause an insured loss of US$78.6 billion, and a Newport-Inglewood magnitude 7.3 would cost an estimated US$77.1 billion according to RMS modeling. These are just a couple of the examples within its stochastic event set with a similar magnitude to the Ridgecrest events and which could have a significant social, economic and insured loss impact if they took place elsewhere in the state.

The RMS model estimates that magnitude 7 earthquakes in California could cause insurance industry losses ranging from US$20,000 to a US$20 billion, but the maximum loss could be over US$100 billion if occurring in high population centers such as Los Angeles. The losses from the Ridgecrest event were on the low side of the range of loss as the event occurred in a less populated area. For the California Earthquake Authority’s portfolio in Los Angeles County, a large loss event of US$10 billion or greater can be expected approximately every 30 years.

As with any major catastrophe, several factors can drive up the insured loss bill, including post-event loss amplification and contingent business interruption, given the potential scale of disruption. In Sacramento, there is also a risk of failure of the levee system.

Fire following earthquake was a significant cause of damage following the 1906 San Francisco Earthquake and was estimated to account for around 40 percent of the overall loss from that event. It is, however, expected that fire would make a much smaller contribution to future events, given modern construction materials and methods and fire suppressant systems. 

Political pressure to settle claims could also drive up the loss total from the event. Lawmakers could put pressure on the CEA and other insurers to settle claims quickly, as has been the case in the aftermath of other catastrophes, such as Hurricane Sandy.

The California Earthquake Authority has recommended homes built prior to 1980 be seismically retrofitted to make them less vulnerable to earthquake damage. “We all need to learn the lesson of Ridgecrest: California needs to be better prepared for the next big earthquake because it’s sure to come,” Pomeroy says.

“We recommend people consider earthquake insurance to protect themselves financially,” he continues. “The government’s not going to come in and rebuild everybody’s home, and a regular residential insurance policy does not cover earthquake damage. The only way to be covered for earthquake damage is to have an additional earthquake insurance policy in place.

 “Close to 90 percent of the state does not have an earthquake insurance policy in place. Let this be the wake-up call that we all need to get prepared.”

Living in a world of constant catastrophes

(Re)insurance companies are waking up to the reality that we are in a riskier world and the prospect of ‘constant catastrophes’ has arrived, with climate change a significant driver

In his hotly anticipated annual letter to shareholders in February 2019, Warren Buffett, the CEO of Berkshire Hathaway and acclaimed “Oracle of Omaha,” warned about the prospect of “The Big One” — a major hurricane, earthquake or cyberattack that he predicted would “dwarf Hurricanes Katrina and Michael.” He warned that “when such a mega-catastrophe strikes, we will get our share of the losses and they will be big — very big.”

“The use of new technology, data and analytics will help us prepare for unpredicted ‘black swan’ events and minimize the catastrophic losses”
— Mohsen Rahnama, RMS

The question insurance and reinsurance companies need to ask themselves is whether they are prepared for the potential of an intense U.S. landfalling hurricane, a Tōhoku-size earthquake event and a major cyber incident if these types of combined losses hit their portfolio each and every year, says Mohsen Rahnama, chief risk modeling officer at RMS. “We are living in a world of constant catastrophes,” he says. “The risk is changing, and carriers need to make an educated decision about managing the risk.

“So how are (re)insurers going to respond to that? The broader perspective should be on managing and diversifying the risk in order to balance your portfolio and survive major claims each year,” he continues. “Technology, data and models can help balance a complex global portfolio across all perils while also finding the areas of opportunity.”

A barrage of weather extremes

How often, for instance, should insurers and reinsurers expect an extreme weather loss year like 2017 or 2018? The combined insurance losses from natural disasters in 2017 and 2018 according to Swiss Re sigma were US$219 billion, which is the highest-ever total over a two-year period. Hurricanes Harvey, Irma and Maria delivered the costliest hurricane loss for one hurricane season in 2017.

Contributing to the total annual insurance loss in 2018 was a combination of natural hazard extremes, including Hurricanes Michael and Florence, Typhoons Jebi, Trami and Mangkhut, as well as heatwaves, droughts, wildfires, floods and convective storms.

While it is no surprise that weather extremes like hurricanes and floods occur every year, (re)insurers must remain diligent about how such risks are changing with respect to their unique portfolios.

Looking at the trend in U.S. insured losses from 1980–2018, the data clearly shows losses are increasing every year, with climate-related losses being the primary drivers of loss, especially in the last four decades (even allowing for the fact that the completeness of the loss data over the years has improved).

Measuring climate change

With many non-life insurers and reinsurers feeling bombarded by the aggregate losses hitting their portfolios each year, insurance and reinsurance companies have started looking more closely at the impact that climate change is having on their books of business, as the costs associated with weather-related disasters increase.

The ability to quantify the impact of climate change risk has improved considerably, both at a macro level and through attribution research, which considers the impact of climate change on the likelihood of individual events. The application of this research will help (re)insurers reserve appropriately and gain more insight as they build diversified books of business.

Take Hurricane Harvey as an example. Two independent attribution studies agree that the anthropogenic warming of Earth’s atmosphere made a substantial difference to the storm’s record-breaking rainfall, which inundated Houston, Texas, in August 2017, leading to unprecedented flooding. In a warmer climate, such storms may hold more water volume and move more slowly, both of which lead to heavier rainfall accumulations over land.

Attribution studies can also be used to predict the impact of climate change on the return-period of such an event, explains Pete Dailey, vice president of model development at RMS. “You can look at a catastrophic event, like Hurricane Harvey, and estimate its likelihood of recurring from either a hazard or loss point of view. For example, we might estimate that an event like Harvey would recur on average say once every 250 years, but in today’s climate, given the influence of climate change on tropical precipitation and slower moving storms, its likelihood has increased to say a 1-in-100-year event,” he explains.

“We can observe an incremental rise in sea level annually — it’s something that is happening right in front of our eyes”
— Pete Dailey, RMS

“This would mean the annual probability of a storm like Harvey recurring has increased more than twofold from 0.4 percent to 1 percent, which to an insurer can have a dramatic effect on their risk management strategy.”

Climate change studies can help carriers understand its impact on the frequency and severity of various perils and throw light on correlations between perils and/or regions, explains Dailey. “For a global (re)insurance company with a book of business spanning diverse perils and regions, they want to get a handle on the overall effect of climate change, but they must also pay close attention to the potential impact on correlated events.

“For instance, consider the well-known correlation between the hurricane season in the North Atlantic and North Pacific,” he continues. “Active Atlantic seasons are associated with quieter Pacific seasons and vice versa. So, as climate change affects an individual peril, is it also having an impact on activity levels for another peril? Maybe in the same direction or in the opposite direction?”

Understanding these “teleconnections” is just as important to an insurer as the more direct relationship of climate to hurricane activity in general, thinks Dailey.

“Even though it’s hard to attribute the impact of climate change to a particular location, if we look at the impact on a large book of business, that’s actually easier to do in a scientifically credible way,” he adds. “We can quantify that and put uncertainty around that quantification, thus allowing our clients to develop a robust and objective view of those factors as a part of a holistic risk management approach.”

Of course, the influence of climate change is easier to understand and measure for some perils than others. “For example, we can observe an incremental rise in sea level annually — it’s something that is happening right in front of our eyes,” says Dailey. “So, sea-level rise is very tangible in that we can observe the change year over year. And we can also quantify how the rise of sea levels is accelerating over time and then combine that with our hurricane model, measuring the impact of sea-level rise on the risk of coastal storm surge, for instance.”

Each peril has a unique risk signature with respect to climate change, explains Dailey. “When it comes to a peril like severe convective storms — tornadoes and hail storms for instance — they are so localized that it’s difficult to attribute climate change to the future likelihood of such an event. But for wildfire risk, there’s high correlation with climate change because the fuel for wildfires is dry vegetation, which in turn is highly influenced by the precipitation cycle.” Satellite data from 1993 through to the present shows there is an upward trend in the rate of sea-level rise, for instance, with the current rate of change averaging about 3.2 millimeters per year. Sea-level rise, combined with increasing exposures at risk near the coastline, means that storm surge losses are likely to increase as sea levels rise more quickly.

“In 2010, we estimated the amount of exposure within 1 meter above the sea level, which was US$1 trillion, including power plants, ports, airports and so forth,” says Rahnama. “Ten years later, the exact same exposure was US$2 trillion. This dramatic exposure change reflects the fact that every centimeter of sea-level rise is subjected to a US$2 billion loss due to coastal flooding and storm surge as a result of even small hurricanes.

“And it’s not only the climate that is changing,” he adds. “It’s the fact that so much building is taking place along the high-risk coastline. As a result of that, we have created a built-up environment that is actually exposed to much of the risk.”

Rahnama highlighted that because of an increase in the frequency and severity of events, it is essential to implement prevention measures by promoting mitigation credits to minimize the risk.  He says: “How can the market respond to the significant losses year after year. It is essential to think holistically to manage and transfer the risk to the insurance chain from primary to reinsurance, capital market, ILS, etc.,” he continues.

“The art of risk management, lessons learned from past events and use of new technology, data and analytics will help to prepare for responding to unpredicted ‘black swan’ type of events and being able to survive and minimize the catastrophic losses.”

Strategically, risk carriers need to understand the influence of climate change whether they are global reinsurers or local primary insurers, particularly as they seek to grow their business and plan for the future. Mergers and acquisitions and/or organic growth into new regions and perils will require an understanding of the risks they are taking on and how these perils might evolve in the future.

There is potential for catastrophe models to be used on both sides of the balance sheet as the influence of climate change grows. Dailey points out that many insurance and reinsurance companies invest heavily in real estate assets. “You still need to account for the risk of climate change on the portfolio, whether you’re insuring properties or whether you actually own them, there’s no real difference.” In fact, asset managers are more inclined to a longer-term view of risk when real estate is part of a long-term investment strategy. Here, climate change is becoming a critical part of that strategy.

“What we have found is that often the team that handles asset management within a (re)insurance company is an entirely different team to the one that handles catastrophe modeling,” he continues. “But the same modeling tools that we develop at RMS can be applied to both of these problems of managing risk at the enterprise level.

“In some cases, a primary insurer may have a one-to-three-year plan, while a major reinsurer may have a five-to-10-year view because they’re looking at a longer risk horizon,” he adds. “Every time I go to speak to a client — whether it be about our new flood model or our North American hurricane model — the question of climate change inevitably comes up. So, it’s become apparent this is no longer an academic question, it’s actually playing into critical business decisions on a daily basis.”


Preparing for a low-carbon economy

Regulation also has an important role in pushing both (re)insurers and large corporates to map and report on the likely impact of climate change on their business, as well as explain what steps they have taken to become more resilient. In the U.K., the Prudential Regulation Authority (PRA) and Bank of England have set out their expectations regarding firms’ approaches to managing the financial risks from climate change. 

Meanwhile, a survey carried out by the PRA found that 70 percent of U.K. banks recognize the risk climate change poses to their business. Among their concerns are the immediate physical risks to their business models — such as the exposure to mortgages on properties at risk of flood and exposure to countries likely to be impacted by increasing weather extremes. Many have also started to assess how the transition to a low-carbon economy will impact their business models and, in many cases, their investment and growth strategy.

“Financial policymakers will not drive the transition to a low-carbon economy, but we will expect our regulated firms to anticipate and manage the risks associated with that transition,” said Bank of England Governor Mark Carney, in a statement.  

The transition to a low-carbon economy is a reality that (re)insurance industry players will need to prepare for, with the impact already being felt in some markets. In Australia, for instance, there is pressure on financial institutions to withdraw their support from major coal projects. In the aftermath of the Townsville floods in February and widespread drought across Queensland, there have been renewed calls to boycott plans for Australia’s largest thermal coal mine.

To date, 10 of the world’s largest (re)insurers have stated they will not provide property or construction cover for the US$15.5 billion Carmichael mine and rail project. And in its “Mining Risk Review 2018,” broker Willis Towers Watson warned that finding insurance for coal “is likely to become increasingly challenging — especially if North American insurers begin to follow the European lead.” 

The future of risk management

(Re)insuring new and emerging risks requires data and, ideally, a historical loss record upon which to manage an exposure. But what does the future of risk management look like when so many of these exposures are intangible or unexpected? 

Sudden and dramatic breakdowns become more likely in a highly interconnected and increasingly polarized world, warns the “Global Risks Report 2019” from the World Economic Forum (WEF). “Firms should focus as much on risk response as on risk mitigation,” advises John Drzik, president of global risk and digital at Marsh, one of the report sponsors. “There’s an inevitability to having a certain number of shock events, and firms should focus on how to respond to fast-moving events with a high degree of uncertainty.”

Macrotrends such as climate change, urbanization and digitization are all combining in a way that makes major claims more impactful when things go wrong. But are all low-probability/high-consequence events truly beyond our ability to identify and manage?

Dr. Gordon Woo, catastrophist at RMS, believes that in an age of big data and advanced analytics, information is available that can help corporates, insurers and reinsurers to understand the plethora of new and emerging risks they face. “The sources of emerging risk insight are out there,” says Woo. “The challenge is understanding the significance of the information available and ensuring it is used to inform decision-makers.”

However, it is not always possible to gain access to the insight needed. “Some of the near-miss data regarding new software and designs may be available online,” says Woo. “For example, with the Boeing 737 Max 8, there were postings by pilots where control problems were discussed prior to the Lion Air disaster of October 2018. Equally, intelligence information on terrorist plots may be available from online terrorist chatter. But typically, it is much harder for individuals to access this information, other than security agencies.

“Peter Drucker [consultant and author] was right when he said: ‘If you can’t measure it, you can’t improve it,’” he adds. “And this is the issue for (re)insurers when it comes to emerging risks. There is currently not a lot of standardization between risk compliance systems and the way the information is gathered, and corporations are still very reluctant to give information away to insurers.”

The intangibles protection gap

While traditional physical risks, such as fire and flood, are well understood, well modeled and widely insured, new and emerging risks facing businesses and communities are increasingly intangible and risk transfer solutions are less widely available.

While there is an important upside to many technological innovations, for example, there are also downsides that are not yet fully understood or even recognized, thinks Robert Muir-Wood, chief research officer of science and technology at RMS.

“Last year’s Typhoon Jebi caused coastal flooding in the Kansai region of Japan,” he says. “There were a lot of cars on the quayside close to where the storm made landfall and many of these just caught on fire. It burnt out a large number of cars that were heading for export.

“The reason for the fires was the improved capability of batteries in cars,” he explains. “And when these batteries are immersed in water they burst into flames. So, with this technology you’ve created a whole new peril.

“There is currently not a lot of standardization between risk compliance systems and the way the information is gathered”
— Gordon Woo, RMS

“As new technology emerges, new risks emerge,” he concludes. “And it’s not as though the old risks go away. They sort of morph and they always will. Clearly the more that software becomes a critical part of how things function, then there is more of an opportunity for things to go wrong.”

From nonphysical-damage business interruption and reputational harm to the theft of intellectual property and a cyber data breach, the ability for underwriters to get a handle on these risks and potential losses is one of the industry’s biggest modern-day challenges. The dearth of products and services for esoteric commercial risks is known as the “intangibles protection gap,” explains Muir-Wood.

“There is this question within the whole span of risk management of organizations — of which an increasing amount is intangible — whether they will be able to buy insurance for those elements of their risk that they feel they do not have control over.”

While the (re)insurance industry is responding with new products and services geared toward emerging risks, such as cyber, there are some organizational perils, such as reputational risk, that are best addressed by instilling the right risk management culture and setting the tone from the top within organizations, thinks Wayne Ratcliffe, head of risk management at SCOR.

“Enterprise risk management is about taking a holistic view of the company and having multidisciplinary teams brainstorming together,” he says. “It’s a tendency of human nature to work in silos in which everyone has their own domain to protect and to work on, but working across an organization is the only way to carry out proper risk management.

“There are many causes and consequences of reputational risk, for instance,” he continues. “When I think of past examples where things have gone horribly wrong — and there are so many of them, from Deepwater Horizon to Enron — in certain cases there were questionable ethics and a failure in risk management culture. Companies have to set the tone at the top and then ensure it has spread across the whole organization. This requires constant checking and vigilance.”

The best way of checking that risk management procedures are being adhered to is by being really close to the ground, thinks Ratcliffe. “We’re moving too far into a world of emails and communication by Skype. What people need to be doing is talking to each other in person and cross-checking facts. Human contact is essential to understanding the risk.”

Spotting the next “black swan”

What of future black swans? As per Donald Rumsfeld’s “unknown unknowns,” so called black swan events are typically those that come from left field. They take everyone by surprise (although are often explained away in hindsight) and have an impact that cascades through economic, political and social systems in ways that were previously unimagined, with severe and widespread consequences.

“As (re)insurers we can look at past data, but you have to be aware of the trends and forces at play,” thinks Ratcliffe. “You have to be aware of the source of the risk. In ‘The Big Short’ by Michael Lewis, the only person who really understood the impending subprime collapse was the one who went house-to-house asking people if they were having trouble paying their mortgages, which they were.

“New technologies are creating more opportunities but they’re also making society more vulnerable to sophisticated cyberattacks”
— Wayne Ratcliffe, SCOR

“Sometimes you need to go out of the bounds of data analytics into a more intuition-based way of picking up signals where there is no data,” he continues. “You need imagination and to come up with scenarios that can happen based on a group of experts talking together and debating how exposures can connect and interconnect.

“It’s a little dangerous to base everything on big data measurement and statistics, and at SCOR we talk about the ‘art and science of risk,’” he continues. “And science is more than statistics. We often need hard science behind what we are measuring. A single-point estimate of the measure is not sufficient. We also need confidence intervals corresponding to a range of probabilities.”

In its “Global Risks Report 2019,” the WEF examines a series of “what-if” future shocks and asks if its scenarios, while not predictions, are at least “a reminder of the need to think creatively about risk and to expect the unexpected?” The WEF believes future shocks could come about as a result of advances in technology, the depletion of global resources and other major macrotrends clashing in new and extreme ways.

“The world is becoming hyperconnected,” says Ratcliffe. “People are becoming more dependent on social media, which is even shaping political decisions, and organizations are increasingly connected via technology and the internet of things. New technologies are creating more opportunities but they’re also making society more vulnerable to sophisticated cyberattacks. We have to think about the systemic nature of it all.”

As governments are pressured to manage the effects of climate change, for instance, will the use of weather manipulation tools — such as cloud seeding to induce or suppress rainfall — result in geopolitical conflict? Could biometrics and AI that recognize and respond to emotions be used to further polarize and/or control society? And will quantum computing render digital cryptography obsolete, leaving sensitive data exposed?

The risk of cyberattack was the No. 1 risk identified by business leaders in virtually all advanced economies in the WEF’s “Global Risks Report 2019,” with concern about both data breach and direct attacks on company infrastructure causing business interruption. The report found that cyberattacks continue to pose a risk to critical infrastructure, noting the attack in July 2018 that compromised many U.S. power suppliers.

In the attack, state-backed Russian hackers gained remote access to utility- company control rooms in order to carry out reconnaissance. However, in a more extreme scenario the attackers were in a position to trigger widespread blackouts across the U.S., according to the Department of Homeland Security.

Woo points to a cyberattack that impacted Norsk Hydro, the company that was responsible for a massive bauxite spill at an aluminum plant in Brazil last year, with a targeted strain of ransomware known as “LockerGoga.” With an apparent motivation to wreak revenge for the environmental damage caused, hackers gained access to the company’s IT infrastructure, including the control systems at its aluminum smelting plants. He thinks a similar type of attack by state-sponsored actors could cause significantly greater disruption if the attackers’ motivation was simply to cause damage to industrial control systems.

Woo thinks cyber risk has significant potential to cause a major global shock due to the interconnected nature of global IT systems. “WannaCry was probably the closest we’ve come to a cyber 911,” he explains. “If the malware had been released earlier, say January 2017 before the vulnerability was patched, losses would have been a magnitude higher as the malware would have spread like measles as there was no herd immunity. The release of a really dangerous cyber weapon with the right timing could be extremely powerful.”

Opening Pandora's Box

With each new stride in hazard research and science comes the ability to better calculate and differentiate risk 

Efforts by RMS scientists and engineers to better understand liquefaction vulnerability is shedding new light on the secondary earthquake hazard. However, this also makes it more likely that, unless they can charge for the risk, (re)insurance appetite will diminish for some locations while also increasing in other areas. A more differentiated approach to underwriting and pricing is an inevitable consequence of investment in academic research.

Once something has been learned, it cannot be unlearned, explains Robert Muir-Wood, chief research officer at RMS. “In the old days, everybody paid the same for insurance because no one had the means to actually determine how risk varied from location to location, but once you learn how to differentiate risk well, there’s just no going back. It’s like Pandora’s box has been opened.

“There are two general types of liquefaction that are just so severe that no one should build on them”
— Tim Ancheta, RMS

“At RMS we are neutral on risk,” he adds. “It’s our job to work for all parties and provide the best neutral science-based perspective on risk, whether that’s around climate change in California or earthquake risk in New Zealand. And we and our clients believe that by having the best science-based assessment of risk they can make effective decisions about their risk management.”

Spotting a gap in the science

On September 28, 2018, a large and shallow M7.5 earthquake struck Central Sulawesi, Indonesia, triggering a tsunami over 2 meters in height. The shaking and tsunami caused widespread devastation in and around the provincial capital Palu, but according to a report published by the GEER Association, it was liquefaction and landslides that caused thousands of buildings to collapse in a catastrophe that claimed over 4,000 lives. It was the latest example of a major earthquake that showed that liquefaction — where the ground moves and behaves as if it is a liquid — can be a much bigger driver of loss than previously thought.

The Tōhoku Earthquake in Japan during 2011 and the New Zealand earthquakes in Christchurch in 2010 and 2011 were other high-profile examples. The earthquakes in New Zealand caused a combined insurance industry loss of US$22.8-US$26.2 billion, with widespread liquefaction undermining the structural integrity of hundreds of buildings. Liquefaction has been identified by a local engineer as causing 50 percent of the loss.

Now, research carried out by RMS scientists is helping insurers and other stakeholders to better understand the impact that liquefaction can have on earthquake-related losses. It is also helping to pinpoint other parts of the world that are highly vulnerable to liquefaction following earthquake.

“Before Christchurch we had not appreciated that you could have a situation where a midrise building may be completely undamaged by the earthquake shaking, but the liquefaction means that the building has suffered differential settlement leaving the floors with a slight tilt, sufficient to be declared a 100 percent loss,” explains Muir-Wood.

“We realized for the first time that you actually have to model the damage separately,” he continues. “Liquefaction is completely separate to the damage caused by shaking. But in the past we treated them as much of the same. Separating out the hazards has big implications for how we go about modeling the risk, or identifying other situations where you are likely to have extreme liquefaction at some point in the future.”

The missing link

Tim Ancheta, a risk modeler for RMS based in Newark, California, is responsible for developing much of the understanding about the interaction between groundwater depth and liquefaction. Using data from the 2011 earthquake in Christchurch and boring data from numerous sites across California to calculate groundwater depth, he has been able to identify sites that are particularly prone to liquefaction.

“I was hired specifically for evaluating liquefaction and trying to develop a model,” he explains. “That was one of the key goals for my position. Before I joined RMS about seven years back, I was a post-doctoral researcher at PEER — the Pacific Earthquake Engineering Research Center at Berkeley — working on ground motion research. And my doctoral thesis was on the spatial variability of ground motions.”

Joining RMS soon after the earthquakes in Christchurch had occurred meant that Ancheta had access to a wealth of new data on the behavior of liquefaction. For the first time, it showed the significance of ground- water depth in determining where the hazard was likely to occur. Research, funded by the New Zealand government, included a survey of liquefaction observations, satellite imagery, a time series of groundwater levels as well as the building responses. It also included data collected from around 30,000 borings.

“All that had never existed on such a scale before,” says Ancheta. “And the critical factor here was they investigated both liquefaction sites and non-liquefaction sites — prior surveys had only focused on the liquefaction sites.”

Whereas the influence of soil type on liquefaction had been reasonably well understood prior to his research, previous studies had not adequately incorporated groundwater depth. “The key finding was that if you don’t have a clear understanding of where the groundwater is shallow or where it is deep, or the transition — which is important — where you go from a shallow to deep groundwater depth, you can’t turn on and off the liquefaction properly when an earthquake happens,” reveals Ancheta.

Ancheta and his team have gone on to collect and digitize groundwater data, geology and boring data in California, Japan, Taiwan and India with a view to gaining a granular understanding of where liquefaction is most likely to occur. “Many researchers have said that liquefaction properties are not regionally dependent, so that if you know the geologic age or types of soils, then you know approximately how susceptible soils can be to liquefaction. So an important step for us is to validate that claim,” he explains.

The ability to use groundwater depth has been one of the factors in predicting potential losses that has significantly reduced uncertainty within the RMS suite of earthquake models, concentrating the losses in smaller areas rather than spreading them over an entire region. This has clear implications for (re)insurers and policymakers, particularly as they seek to determine whether there are any “no-go” areas within cities.

“There are two general types of liquefaction that are just so severe that no one should build on them,” says Ancheta. “One is lateral spreading where the extensional strains are just too much for buildings. In New Zealand, lateral spreading was observed at numerous locations along the Avon River, for instance.”

California is altogether more challenging, he explains. “If you think about all the rivers that flow through Los Angeles or the San Francisco Bay Area, you can try and model them in the same way as we did with the Avon River in Christchurch. We discovered that not all rivers have a similar lateral spreading on either side of the riverbank. Where the river courses have been reworked with armored slopes or concrete linings — essentially reinforcement — it can actually mitigate liquefaction-related displacements.”

The second type of severe liquefaction is called “flow slides” triggered by liquefaction, which is where the soil behaves almost like a landslide. This was the type of liquefaction that occurred in Central Sulawesi when the village of Balaroa was entirely destroyed by rivers of soil, claiming entire neighborhoods.

“It’s a type of liquefaction that is extremely rare,” he adds. “but they can cause tens to hundreds of meters of displacement, which is why they are so devastating. But it’s much harder to predict the soils that are going to be susceptible to them as well as you can for other types of liquefaction surface expressions.”

Ancheta is cognizant of the fact that a no-build zone in a major urban area is likely to be highly contentious from the perspective of homeowners, insurers and policymakers, but insists that now the understanding is there, it should be acted upon.

“The Pandora’s box for us in the Canterbury Earthquake Sequence was the fact that the research told us where the lateral spreading would occur,” he says. “We have five earthquakes that produced lateral spreading so we knew with some certainty where the lateral spreading would occur and where it wouldn’t occur. With severe lateral spreading you just have to demolish the buildings affected because they have been extended so much.”

The future for flood protection

With innovation in the flood market increasing, EXPOSURE explores whether high-definition (HD) flood models are one of the keys to closing the protection gap

In August 2017, Hurricane Harvey brought the highest level of rainfall associated with a tropical cyclone in the U.S. since records began, causing catastrophic flooding in some of the most populated areas of the Texas coast, including Houston. The percentage of losses attributed to inland flood versus wind damage was significant, altering the historical view that precipitation resulting from a tropical storm or hurricane is an attritional loss and highlighting the need for stochastic modeling.

Total economic losses resulting from Harvey were around US$85 billion and insured losses were US$30 billion, revealing a significant protection gap, particularly where inland flood damage was concerned. Around 200,000 homes were inundated by the floods, and yet 80 percent of homes in the Houston area were uninsured.

Hurricane Harvey Impacts - Aftermath

Now, an innovative catastrophe bond suggests one way this protection gap could be reduced in the future, particularly as a private flood insurance market develops in the U.S. FloodSmart Re, which was announced at the end of July, secured US$500 million of reinsurance protection on behalf of FEMA’s National Flood Insurance Program (NFIP). Reinsurer Hannover Re was acting as the ceding reinsurer for the transaction, sitting between the NFIP and its Bermuda-based special purpose insurer.

“It’s a landmark transaction — the first time in history that the U.S. federal government is sponsoring a catastrophe bond,” says John Seo, co-founder and managing principal at Fermat Capital. “It’s just tremendous and I couldn’t be more excited. Events like Harvey are going to accelerate the development of the flood market in terms of risk transfer to the insurance-linked securities (ILS) market.

“You have to have more efficient risk pooling and risk sharing mechanisms,” he adds. “There’s over US$200 trillion dollars of capital in the world, so there’s obviously enough to efficiently absorb event risk. So, it’s about, how do you get it out into that larger capital base in an efficient way?”

While the bond only provides cover for flooding arising from named storms, either due to storm surge or rainfall, it is a “good test case for the ILS market’s appetite for flood risks,” according to ILS blog Artemis. While “it is not a broad flood coverage, it will likely help to make it more palatable to cat bond investors given their comfort with modeling the probability of named storms, tropical storms and hurricanes.”

According to Cory Anger, global head of ILS origination and structuring at GC Securities, the ILS market is certainly showing an appetite for flood risk — including inland flood risk ­— with several catastrophe bonds completed over the last year for European flood risk (Generali’s Lion II), Japanese flood risk (MSI and ADI’s Akibare Series 2018-1 Notes) and U.S. flood risk.

“Both public and private sector entities see value from utilizing capital markets’ capacity to manage flood risk,” she says. “We think there are other geographic regions that would be interested in ILS capacity that haven’t yet tapped the ILS markets. Given the recent success of FEMA/NFIP’s FloodSmart Re Series 2018-1 Notes, we expect FEMA/NFIP to continue to utilize ILS capacity (along with traditional reinsurance capital) to support future U.S. flood risk transfer opportunities.”

The ILS sector has grown significantly over the past 15 years, with deals becoming more complex and innovative over time. Many market commentators feel the market was put to the test following the major natural catastrophe losses in 2017. Not only did bonds pay out where they were triggered, fresh capital re-entered, demonstrating investors’ confidence in the sector and its products.

“I’m hearing people starting to coin the phrase that 2018 is the ‘great reload,’” says Seo. “This is something I have been saying for quite some years: That the traditional hard-soft, soft-hard market cycle is over. It’s not that you can’t have an event so large that it doesn’t impact the market, but when it comes to capital markets, high yields are actually a siren call for capital.

“I don’t think anyone doubts that had 2017 occurred in the absence of the ILS market it would have been a completely different story, and we would have had a traditional hard market scenario in 2018,” he adds.

FloodSmart Re has clearly demonstrated the strong investor interest in such transactions. According to Anger, GC Securities acted as the structuring agent for the transaction and was one of two book runners. More than 35 capital markets investors provided fully collateralized protection to FEMA/NFIP on the landmark catastrophe bond.

“The appetite for new perils is generally strong, so there’s always strong interest when new risks are brought to market,” says Ben Brookes, managing director of capital and resilience solutions at RMS.

He thinks improvements in the underlying data quality along with high-definition flood models make it more likely that inland flood could be included as a peril in future catastrophe bond issuances on behalf of private insurers, on an indemnity basis.

“In the early days of the cat bond market, new perils would typically be issued with parametric triggers, because investors were skeptical that sufficient data quality was achieved or that the indemnity risks were adequately captured by cat models. But that changed as investor comfort grew, and a lot of capital entered the market and you saw all these deals becoming indemnity. Increased comfort with risk modeling was a big part of that.”

The innovative Blue Wings catastrophe bond, which covered insurer Allianz for severe U.K. flood risk (and some U.S. and Canadian quake) and was completed in 2007, is a good example. The parametric bond used an index to calculate flood depths at over 50 locations across the U.K., was ahead of its time and is the only U.K. flood catastrophe bond that has come to market.

According to Anger, as models have become more robust for flood risk — whether due to tropical cyclone (storm surge and excess precipitation) or inland flooding (other than from tropical cyclone) ­— the investor base has been open to trigger selection (e.g., indemnity or parametric).

“In general, insurers are preferring
indemnity-triggered solutions,” she adds, “which the ILS market has concurrently been open to. Additionally, for this peril, the ILS community has been open to per occurrence and annual aggregate structures, which gives flexibility to sponsors to incorporate ILS capital in their risk transfer programs.”

As the private market develops, cat bond sponsors from the insurance market would be more likely to bundle inland flood risk in with other perils, thinks Charlotte Acton, director of capital and resilience solutions at RMS. “A degree of hurricane-induced
inland flood risk is already present on a non-
modeled basis within some transactions in the market,” she says. “And Harvey illustrates the value in comprehensive modeling of flooding associated with named storms.

“So, for a broader portfolio, in most cases, inland flood would be one piece of the picture as it will be exposed to multiple perils. However, a stand-alone inland flood bond is possible for a public sector or corporate sponsor that has specific exposure to flood risk.”

With inland flood, as with all other perils, sophisticated models help to make markets. “A fund would look at the risk in and of itself in the deal, but of course they’d also want to understand the price and returns perspective as well,” says Brookes. “Models play into that quite heavily. You can’t price a bond well, and understand the returns of a bond, unless you understand the risk of it.”

As the ILS market makes increasing use of indemnity protection through ultimate net loss (UNL) triggers, sophisticated HD flood modeling will be essential in order to transfer the peril to the capital markets. This allows clear parameters to be set around different hours clauses and deductible structures, for instance, in addition to modeling all causes of flood and the influence of local defenses.

“It’s a landmark transaction — the first time in history that the U.S. Federal Government is sponsoring a catastrophe bond” — John SEO, Fermat capital

Jillian Williams, head of portfolio analysis at Leadenhall Capital Partners, notes that ILS is increasingly bundling together multiple perils in an effort to gain diversification.

“Diversification is important for any investment strategy, as you are always trying to minimize the risk of losing large amounts in one go,” she says. “Cat bonds (144A’s) currently have defined perils, but collateralized reinsurance and private cat bonds can cover all perils. Complexities and flow of information to all parties will be a challenge for cat bonds to move from defined perils to UNL all perils.

“Any new peril or structure in a cat bond will generate many questions, even if they don’t have a major impact on the potential losses,” she continues. “Investors will want to know why the issuers want to include these new perils and structures and how the associated risk is calculated. For UNL, all flood (not just sea surge) would be included in the cat bond, so the definition of the peril, its complexities, variables and its correlation to other perils will need to be evaluated and represented in the flood models used.”

She thinks the potential to transfer more flood to the capital markets is there, but that the complexity of the peril are challenges that need to be overcome, particularly in the U.S. “Flood coverage is already starting to move into the capital markets, but there are many issues that need to be worked through before it can be moved to a 144A transaction in a UNL format for many territories,” says Williams. “Just one of the complexities is that flood risk may be covered by government pools.

“To move flood perils from government pools to private insurers is like any evolution, it can take time, particularly if existing coverage is subsidized,” she adds. “For private insurers, the complexity is not just about flood modeling but also about ensuring risk-adequate pricing and navigating through government legislation.”

When the lights went out

How poor infrastructure, grid blackouts and runaway business interruption has hampered Puerto Rico’s recovery in the aftermath of Hurricane Maria

As the 2018 North Atlantic hurricane season continues, Puerto Rico has yet to recover from destructive events of the previous year. In September 2017, Category 4 Hurricane Maria devastated several Caribbean islands, including Puerto Rico, and left a trail of destruction in its path. For many, Maria was one of the worst natural catastrophes to hit a U.S. territory, causing an estimated US$65 billion to US$115 billion in damage and claiming as many as 4,500 to 5,000 lives.

The damage wrought has further strained the island’s sluggish economy. Puerto Rico had over US$70 billion in public debt when Maria hit. Economic forecasts for 2018 to 2020, considering the impact of Hurricane Maria, suggest Puerto Rico’s GDP will decline by 7 to 8 percent in 2018 and likely remain in a negative range of 5 to 7 percent for the next few years.

“Resilience is also about the financial capacity to come back and do the reconstruction work” — Pooya Sarabandi, RMS

Power outages, business interruption (BI) and contingent BI (CBI) — including supply chain disruption — have hampered the economy’s recovery. “Resilience is also about the financial capacity to come back and do the reconstruction work,” explains Pooya Sarabandi, global head of data analy-
tics at RMS. “You’re now into this chicken-
and-egg situation where the Puerto Rican government already has a lot of public debt and doesn’t have reserves, and meanwhile the federal U.S. government is only willing to provide a certain level of funding.”

Maria’s devastating impact on Puerto Rico demonstrates the lasting effect a major catastrophe can have when it affects a small, isolated region with a concentrated industry and lack of resilience in infrastructure and lifelines. Whereas manufacturers based on the U.S. mainland have contingencies to tap into — the workforce, raw materials and components, and infrastructure in other parts of the country during times of need — there is not the same opportunity to do this on an island, explains Sarabandi.

Rolling blackouts

Following Maria’s landfall, residences and businesses experienced power outages throughout the island. Severe physical damage to electric power generation plants, transmission and distribution systems — including solar and wind power generation plants — plunged the island into a prolonged period of rolling blackouts.

Around 80 percent of utility poles were damaged in the event, leaving most of the island without electricity. Two weeks after the storm, 90 percent of the island was still without power. A month on, roughly 85 percent of customers were not connected to the power grid. Three months later, this figure was reported to be about half of Puerto Ricans. And finally, after six months, about 15 percent of residents did not have electricity.

“There’s no real damage on the grid itself,” says Victor Roldan, head of Caribbean and Latin America at RMS. “Most of the damage is on the distribution lines around the island. Where they had the better infrastructure in the capital, San Juan, they were able to get it back up and running in about two weeks. But there are still parts of the island without power due to bad distribution infrastructure. And that’s where the business interruption is mostly coming from.

“There are reports that 50 percent of all Maria claims for Puerto Rico will be CBI related,” adds Roldan. “Insurers were very competitive, and CBI was included in commercial policies without much thought to the consequences. Policyholders probably paid a fifth of the premiums they should have, way out of kilter with the risk. The majority of CBI claims will be power related, the businesses didn’t experience physical damage, but the loss of power has hit them financially.”

Damage to transportation infrastructure, including railways and roads, only delayed the pace of recovery. The Tren Urbano, the island’s only rail line that serves the San Juan metropolitan area (where roughly 60 percent of Puerto Ricans live), started limited service for the first time almost three months after Hurricane Maria struck. There were over 1,500 reported instances of damage to roads and bridges across the island. San Juan’s main airport, the busiest in the Caribbean, was closed for several weeks.

A concentration of risk

Roughly half of Puerto Rico’s economy is based on manufacturing activities, with around US$50 billion in GDP coming from industries such as pharmaceutical, medical devices, chemical, food, beverages and tobacco. Hurricane Maria had a significant impact on manufacturing output in Puerto Rico, particularly on the pharmaceutical and medical devices industries, which is responsible for 30 percent of the island’s GDP.

According to Anthony Phillips, chairman of Willis Re Latin America and Caribbean, the final outcome of the BI loss remains unknown but has exceeded expectations due to the length of time in getting power reinstalled. “It’s hard to model the BI loss when you depend on the efficiency of the power companies,” he says. “We used the models and whilst personal lines appeared to come in within expectations, commercial lines has exceeded them. This is mainly due to BI and the inability of the Puerto Rico Electric Power Authority (PREPA) to get things up and running.”

Home to more than 80 pharmaceutical manufacturing facilities, many of which are operated by large multinational companies, Puerto Rico’s pharmaceutical hub was a significant aggregation of risk from a supply chain and insurance perspective. Although only a few of the larger pharmaceutical plants were directly damaged by the storm, operations across the sector were suspended or reduced, in some cases for weeks or even months, due to power outages, lack of access and logistics.

“The perception of the Business Interruption insurers anticipated, versus the reality, was a complete mismatch” — Mohsen Rahnama, RMS

“The perception of the BI insurers anticipated, versus the reality, was a complete mismatch,” says Mohsen Rahnama, chief risk modeling officer at RMS. “All the big names in pharmaceuticals have operations in Puerto Rico because it’s more cost-
effective for production. And they’re all global companies and have backup processes in place and cover for business interruption. However, if there is no diesel on the island for their generators, and if materials cannot get to the island, then there are implications across the entire chain of supply.”

While most of the plants were equipped with backup power generation units, manu-
facturers struggled due to long-term lack of connection to the island’s only power grid. The continuous functioning of on-site generators was not only key to resuming production lines, power was also essential for refrigeration and storage of the pharmaceuticals. Five months on, 85 medicines in the U.S. were classified by the Food and Drug Administration (FDA) as “in shortage.”

There are several reasons why Puerto Rico’s recovery stalled. Its isolation from the U.S. mainland and poor infrastructure were both key factors, highlighted by comparing the island’s recovery to recovery operations following U.S. mainland storms, such as Hurricane Harvey in Texas last year and 2012’s Superstorm Sandy.

Not only did Sandy impact a larger area when it hit New York and New Jersey, it also caused severe damage to all transmission and distribution systems in its path. However, recovery and restoration took weeks, not months.

It is essential to incorporate the vulnerabilities created by an aggregation of risk, inadequate infrastructure and lack of contingency options into catastrophe and pricing models, thinks Roldan. “There is only one power company and the power company is facing bankruptcy,” he says. “It hasn’t invested in infrastructure in years. Maria wasn’t even the worst-case scenario because it was not a direct hit to San Juan. So, insurers need to be prepared and underwriting business interruption risks in a more sophisticated manner and not succumbing to market pressures.”

CBI impact on hospitality and tourism

Large-magnitude, high-consequence events have a lasting impact on local populations. Businesses can face increased levels of disruption and loss of revenue due to unavailability of customers, employees or both. These resourcing issues need to be properly considered in the scenario-planning stage, particularly for sectors such as hospitality and tourism.

Puerto Rico’s hospitality and tourism sectors are a significant source of its GDP. While 69 percent of hotels and 61 percent of casinos were operational six weeks after Maria struck, according to the Puerto Rico Tourism Company, other factors continued to deter visitors. 

It was not until the end of February 2018, five months after the event, that roughly 80 percent of Puerto Rico’s hotels and restaurants were back in business with tourists returning to the island. This suggests a considerable loss of income due to indirect business interruption in the hospitality and tourism industry. 

IFRS 17: Under the microscope

How new accounting standards could reduce demand for reinsurance as cedants are forced to look more closely at underperforming books of business

They may not be coming into effect until January 1, 2021, but the new IFRS 17 accounting standards are already shaking up the insurance industry. And they are expected to have an impact on the January 1, 2019, renewals as insurers ready themselves for the new regime.

Crucially, IFRS 17 will require insurers to recognize immediately the full loss on any unprofitable insurance business. “The standard states that reinsurance contracts must now be valued and accounted for separate to the underlying contracts, meaning that traditional ‘netting down’ (gross less reinsured) and approximate methods used for these calculations may no longer be valid,” explained PwC partner Alex Bertolotti in a blog post.

“Even an individual reinsurance contract could be material in the context of the overall balance sheet, and so have the potential to create a significant mismatch between the value placed on reinsurance and the value placed on the underlying risks,” he continued.

“This problem is not just an accounting issue, and could have significant strategic and operational implications as well as an impact on the transfer of risk, on tax, on capital and on Solvency II for European operations.”

In fact, the requirements under IFRS 17 could lead to a drop in reinsurance purchasing, according to consultancy firm Hymans Robertson, as cedants are forced to question why they are deriving value from reinsurance rather than the underlying business on unprofitable accounts. “This may dampen demand for reinsurance that is used to manage the impact of loss making business,” it warned in a white paper.

Cost of compliance

The new accounting standards will also be a costly compliance burden for many insurance companies. Ernst & Young estimates that firms with over US$25 billion in Gross Written Premium (GWP) could be spending over US$150 million preparing for IFRS 17.

Under the new regime, insurers will need to account for their business performance at a more granular level. In order to achieve this, it is important to capture more detailed information on the underlying business at the point of underwriting, explained Corina Sutter, director of government and regulatory affairs at RMS.

This can be achieved by deploying systems and tools that allow insurers to capture, manage and analyze such granular data in increasingly high volumes, she said. “It is key for those systems or tools to be well-integrated into any other critical data repositories, analytics systems and reporting tools.

“From a modeling perspective, analyzing performance at contract level means precisely understanding the risk that is being taken on by insurance firms for each individual account,” continued Sutter. “So, for P&C lines, catastrophe risk modeling may be required at account level. Many firms already do this today in order to better inform their pricing decisions. IFRS 17 is a further push to do so.

“It is key to use tools that not only allow the capture of the present risk, but also the risk associated with the future expected value of a contract,” she added. “Probabilistic modeling provides this capability as it evaluates risk over time.”

What one thing would... help improve the level of uncertainty when assessing risks?

Gregory Lowe

Global Head of Resilience and Sustainability, Aon

One thing that aspects of climate change are telling us is that past experience may not be reflective of what the future holds. Whether that means greater or fewer losses, we don’t always know as there are so many variables at play. But it is clear that as more uncertainty and complexity is introduced into a system, this creates a society that’s very vulnerable to shocks.

There is complexity at the climate level — because we are in uncharted territory with feedback loops, etc. — and complexity within the society that we’ve built around us, which is so dependent on interlinked infrastructure and technology. One story around Florida has been that the improvement in building codes since Hurricane Andrew has made a tremendous difference to the losses.

There is also this trade-off in how you deal with exposure to multiple hazards and underwrite that risk. So, if you’re making a roof wind resistant does that have an impact on seismic resistance? Does one peril exacerbate another? In California, we’ve seen some large flood events and wildfires, and there’s a certain interplay there when you experience extremes from one side and the other.

We can’t ignore the socio-economic as well as the scientific and climate-related factors when considering the risk. While the industry talks a lot about systemic risk, we are still a long way off from really addressing that. And you’re never going to underwrite systemic risk as such, but thinking about how one risk could potentially impact another is something that we all need to get better at.

Every discipline or industry is based upon a set of assumptions. And it’s not that we should necessarily throw our assumptions out the window, but we should have a sense of when we need to change those. Certainly, the assumption that you have this relatively stable environment with the occasional significant loss year is one to consider. Volatility is something I would expect to see a lot more of in the future.

David Flandro

Head of Global Analytics, JLT Re

It’s key for underwriters to understand the importance of the ranges in model outputs and to interpret the data as best they can. Of course, model vendors can help interpret the data, but at the end of the day it’s the underwriter who must make the decision. The models are there to inform underwriting decisions, not to make underwriting decisions. I think sometimes people use them for the latter, and that’s when they get into trouble.

There was noticeable skepticism around modeled loss ranges released in the wake of Hurricanes Harvey, Irma and Maria in 2017. So clearly, there was an opportunity to explore how the industry was using the models. What are we doing right? What could we be doing differently?

One thing that could improve catastrophe model efficacy is improving the way that they are understood. Better communication on the part of the modeling firms could improve outcomes. This may sound qualitative, but we’ve got a lot of very quantitative people in the industry and they don’t always get it right.

It’s also incumbent on the modeling firms to continue to learn to look at their own output empirically over a long period of time and understand where they got it right, where they got it wrong and then show everybody how they’re learning from it. And likewise, underwriters need to understand the modelers are not aiming for metaphysical accuracy, but for sensible estimates and ranges. These are supposed to be starting points, not endpoints.

Taking cloud adoption to the core

Insurance and reinsurance companies have been more reticent than other business sectors in embracing Cloud technology. EXPOSURE explores why it is time to ditch “the comfort blanket”

The main benefits of Cloud computing are well-established and include scale, efficiency and cost effectiveness. The Cloud also offers economical access to huge amounts of computing power, ideal to tackle the big data/big analytics challenge. And exciting innovations such as microservices — allowing access to prebuilt, Cloud-hosted algorithms, artificial intelligence (AI) and machine learning applications, which can be assembled to build rapidly deployed new services — have the potential to transform the (re)insurance industry.

And yet the industry has continued to demonstrate a reluctance in moving its core services onto a Cloud-based infrastructure. While a growing number of insurance and reinsurance companies are using Cloud services (such as those offered by Amazon Web Services, Microsoft Azure and Google Cloud) for nonessential office and support functions, most have been reluctant to consider Cloud for their mission-critical infrastructure.

In its research of Cloud adoption rates in regulated industries, such as banking, insurance and health care, McKinsey found, “Many enterprises are stuck supporting both their inefficient traditional data-center environments and inadequately planned Cloud implementations that may not be as easy to manage or as affordable as they imagined.”

No magic bullet

It also found that “lift and shift” is not enough, where companies attempt to move existing, monolithic business applications to the Cloud, expecting them to be “magically endowed with all the dynamic features.”

“We’ve come up against a lot of that when explaining the difference in what the RMS(one)® platform offers,” says Farhana Alarakhiya, vice president of products at RMS. “Basically, what clients are showing us is their legacy offering placed on a new Cloud platform. It’s potentially a better user interface, but it’s not really transforming the process.”

Now is the time for the market-leading (re)insurers to make that leap and really transform how they do business, she says. “It’s about embracing the new and different and taking comfort in what other industries have been able to do. A lot of Cloud providers are making it very easy to deliver analytics on the Cloud. So, you’ve got the story of agility, scalability, predictability, compliance and security on the Cloud and access to new analytics, new algorithms, use of microservices when it comes to delivering predictive analytics.”

This ease to tap into highly advanced analytics and new applications, unburdened from legacy systems, makes the Cloud highly attractive. Hussein Hassanali, managing partner at VTX Partners, a division of Volante Global, commented: “Cloud can also enhance long-term pricing adequacy and profitability driven by improved data capture, historical data analytics and automated links to third-party market information. Further, the ‘plug-and-play’ aspect allows you to continuously innovate by connecting to best-in-class third-party applications.”

While moving from a server-based platform to the Cloud can bring numerous advantages, there is a perceived unwillingness to put high-value data into the environment, with concerns over security and the regulatory implications that brings.

This includes data protection rules governing whether or not data can be moved across borders. “There are some interesting dichotomies in terms of attitude and reality,” says Craig Beattie, analyst at Celent Consulting. “Cloud-hosting providers in western Europe and North America are more likely to have better security than (re)insurers do in their internal data centers, but the board will often not support a move to put that sort of data outside of the company’s infrastructure.

“Today, most CIOs and executive boards have moved beyond the knee-jerk fears over security, and the challenges have become more practical,” he continues. “They will ask, ‘What can we put in the Cloud? What does it cost to move the data around and what does it cost to get the data back? What if it fails? What does that backup look like?’”

With a hybrid Cloud solution, insurers wanting the ability to tap into the scalability and cost efficiencies of a
software-as-a-service (SaaS) model, but unwilling to relinquish their data sovereignty, dedicated resources can be developed in which to place customer data alongside the Cloud infrastructure. But while a private or hybrid solution was touted as a good compromise for insurers nervous about data security, these are also more costly options. The challenge is whether the end solution can match the big Cloud providers with global footprints that have compliance and data sovereignty issues already covered for their customers.

“We hear a lot of things about the Internet being cheap — but if you partially adopt the Internet and you’ve got significant chunks of data, it gets very costly to shift those back and forth,” says Beattie.

A Cloud-first approach

Not moving to the Cloud is no longer a viable option long term, particularly as competitors make the transition and competition and disruption change the industry beyond recognition. Given the increasing cost and complexity involved in updating and linking legacy systems and expanding infrastructure to encompass new technology solutions, Cloud is the obvious choice for investment, thinks Beattie.

“If you’ve already built your on-premise infrastructure based on classic CPU-based processing, you’ve tied yourself in and you’re committed to whatever payback period you were expecting,” he says. “But predictive analytics and the infrastructure involved is moving too quickly to make that capital investment. So why would an insurer do that? In many ways it just makes sense that insurers would move these services into the Cloud.

“State-of-the-art for machine learning processing 10 years ago was grids of generic CPUs,” he adds. “Five years ago, this was moving to GPU-based neural network analyses, and now we’ve got ‘AI chips’ coming to market. In an environment like that, the only option is to rent the infrastructure as it’s needed, lest we invest in something that becomes legacy in less time than it takes to install.”

Taking advantage of the power and scale of Cloud computing also advances the march toward real-time, big data analytics. Ricky Mahar, managing partner at VTX Partners, a division of Volante Global, added: “Cloud computing makes companies more agile and scalable, providing flexible resources for both power and space. It offers an environment critical to the ability of companies to fully utilize the data available and capitalize on real-time analytics. Running complex analytics using large data sets enhances both internal decision-making and profitability.”

As discussed, few (re)insurers have taken the plunge and moved their mission-critical business to a Cloud-based SaaS platform. But there are a handful. Among these first movers are some of the newer, less legacy-encumbered carriers, but also some of the industry’s more established players. The latter includes U.S.-based life insurer MetLife, which announced it was collaborating with IBM Cloud last year to build a platform designed specifically for insurers. Meanwhile Munich Re America is offering a Cloud-hosted AI platform to its insurer clients. “The ice is thawing and insurers and reinsurers are changing,” says Beattie. “Reinsurers [like Munich Re] are not just adopting Cloud but are launching new innovative products on the Cloud.”

What’s the danger of not adopting the Cloud? “If your reasons for not adopting the Cloud are security-based, this reason really doesn’t hold up any more. If it is about reliability, scalability, remember that the largest online enterprises such as Amazon, Netflix are all Cloud-based,” comments Farhana Alarakhiya. “The real worry is that there are so many exciting, groundbreaking innovations built in the Cloud for the (re)insurance industry, such as predictive analytics, which will transform the industry, that if you miss out on these because of outdated fears, you will damage your business. The industry is waiting for transformation, and it’s progressing fast in the Cloud.”

Are we moving off the baseline?

How is climate change influencing natural perils and weather extremes, and what should reinsurance companies do to respond?

Reinsurance companies may feel they are relatively insulated from the immediate effects of climate change on their business, given that most property catastrophe policies are renewed on an annual basis. However, with signs that we are already moving off the historical baseline when it comes to natural perils, there is evidence to suggest that underwriters should already be selectively factoring the influence of climate change into their day-to-day decision-making.

Most climate scientists agree that some of the extreme weather anticipated by the United Nations Intergovernmental Panel on Climate Change (IPCC) in 2013 is already here and can be linked to climate change in real time via the burgeoning field of extreme weather attribution. “It’s a new area of science that has grown up in the last 10 to 15 years,” explains Dr. Robert Muir-Wood, chief research officer at RMS. “Scientists run two climate models for the whole globe, both of them starting in 1950. One keeps the atmospheric chemistry static since then, while the other reflects the actual increase in greenhouse gases. By simulating thousands of years of these alternative worlds, we can find the difference in the probability of a particular weather extreme.”

"Underwriters should be factoring the influence of climate change into their day-to-day decision-making"

For instance, climate scientists have run their models in an effort to determine how much the intensity of the precipitation that caused such devastating flooding during last year’s Hurricane Harvey can be attributed to anthropogenic climate change. Research conducted by scientists at the World Weather Attribution (WWA) project has found that the record rainfall produced by Harvey was at least three times more likely to be due to the influence of global warming.

This suggests, for certain perils and geographies, reinsurers need to be considering the implications of an increased potential for certain climate extremes in their underwriting. “If we can’t rely on the long-term baseline, how and where do we modify our perspective?” asks Muir-Wood. “We need to attempt to answer this question peril by peril, region by region and by return period. You cannot generalize and say that all perils are getting worse everywhere, because they’re not. In some countries and perils there is evidence that the changes are already material, and then in many other areas the jury is out and it’s not clear.”

Keeping pace with the change

While the last IPCC report was published five years ago (the next one is due in 2019), there is some consensus on how climate change is beginning to influence natural perils and climate extremes. Many regional climates naturally have large variations at interannual and even interdecadal timescales, which makes observation of climate change, and validation of predictions, more difficult.

“There is always going to be uncertainty when it comes to climate change,” emphasizes Swenja Surminski, head of adaptation research at the Grantham Research Institute on Climate Change and the Environment, part of the London School of Economics and Political Science (LSE). “But when you look at the scientific evidence, it’s very clear what’s happening to temperature, how the average temperature is increasing, and the impact that this can have on fundamental things, including extreme events.”

According to the World Economic Forum’s Global Risks Report 2018, “Too little has been done to mitigate climate change and ... our own analysis shows that the likelihood of missing the Paris Agreement target of limiting global warming to two degrees Celsius or below is greater than the likelihood of achieving it.”

The report cites extreme weather events and natural disasters as the top two “most likely” risks to happen in the next 10 years and the second- and third-highest risks (in the same order) to have the “biggest impact” over the next decade, after weapons of mass destruction. The failure of climate change mitigation and adaptation is also ranked in the top five for both likelihood and impact. It notes that 2017 was among the three hottest years on record and the hottest ever without an El Niño.

It is clear that climate change is already exacerbating climate extremes, says Surminski, causing dry regions to become drier and hot regions to become hotter. “By now, based on our scientific understanding and also thanks to modeling, we get a much better picture of what our current exposure is and how that might be changing over the next 10, 20, even 50 to 100 years,” she says.

“There is also an expectation we will have more freak events, when suddenly the weather produces really unexpected, very unusual phenomena,” she continues. “That’s not just climate change. It’s also tied into El Niño and other weather phenomena occurring, so it’s a complex mix. But right now, we’re in a much better position to understand what’s going on and to appreciate that climate change is having an impact.”

Pricing for climate change

For insurance and reinsurance underwriters, the challenge is to understand the extent to which we have already deviated from the historical record and to manage and price for that appropriately. It is not an easy task given the inherent variability in existing weather patterns, according to Andy Bord, CEO of Flood Re, the U.K.’s flood risk pool, which has a panel of international reinsurers.

“The existing models are calibrated against data that already includes at least some of the impact of climate change,” he says. “Some model vendors have also recently produced models that aim to assess the impact of climate change on the future level of flood risk in the U.K. We know at least one larger reinsurer has undertaken their own climate change impact analyses.

“We view improving the understanding of the potential variability of weather given today’s climate as being the immediate challenge for the insurance industry, given the relatively short-term view of markets,” he adds.

The need for underwriters to appreciate the extent to which we may have already moved off the historical baseline is compounded by the conflicting evidence on how climate change is influencing different perils. And by the counterinfluence or confluence, in many cases, of naturally occurring climate patterns, such as El Niño and the Atlantic Multidecadal Oscillation (AMO).

The past two decades have seen below-normal European windstorm activity, for instance, and evidence builds that the unprecedented reduction in Arctic sea ice during the autumn months is the main cause, according to Dr. Stephen Cusack, director of model development at RMS. “In turn, the sea ice declines have been driven both by the ‘polar amplification’ aspect of anthropogenic climate change and the positive phase of the AMO over the past two decades, though their relative roles are uncertain.

“We view improving the understanding of the potential variability of weather given today’s climate as being the immediate challenge for the insurance industry, given the relatively short-term view of markets” — Andy Bord, Flood Re

“The (re)insurance market right now is saying, ‘Your model has higher losses than our recent experience.’ And what we are saying is that the recent lull is not well understood, and we are unsure how long it will last. Though for pricing future risk, the question is when, and not if, the rebound in European windstorm activity happens. Regarding anthropogenic climate change, other mechanisms will strengthen and counter the currently dominant ‘polar amplification’ process. Also, the AMO goes into positive and negative phases,” he continues. “It’s been positive for the last 20 to 25 years and that’s likely to change within the next decade or so.”

And while European windstorm activity has been somewhat muted by the AMO, the same cannot be said for North Atlantic hurricane activity. Hurricanes Harvey, Irma and Maria (HIM) caused an estimated US$92 billion in insured losses, making 2017 the second costliest North Atlantic hurricane season, according to Swiss Re Sigma. “The North Atlantic seems to remain in an active phase of hurricane activity, irrespective of climate change influences that may come on top of it,” the study states.

While individual storms are never caused by one factor alone, stressed the Sigma study, “Some of the characteristics observed in HIM are those predicted to occur more frequently in a warmer world.” In particular, it notes the high level of rainfall over Houston and hurricane intensification. While storm surge was only a marginal contributor to the losses from Hurricane Harvey, Swiss Re anticipates the probability of extreme storm surge damage in the northeastern U.S. due to higher seas will almost double in the next 40 years.

“From a hurricane perspective, we can talk about the frequency of hurricanes in a given year related to the long-term average, but what’s important from the climate change point of view is that the frequency and the intensity on both sides of the distribution are increasing,” says Dr. Pete Dailey, vice president at RMS. “This means there’s more likelihood of quiet years and more likelihood of very active years, so you’re moving away from the mean, which is another way of thinking about moving away from the baseline.

“So, we need to make sure that we are modeling the tail of the distribution really well, and that we’re capturing the really wet years — the years where there’s a higher frequency of torrential rain in association with events that we model.”

The edge of insurability

Over the long term, the industry likely will be increasingly insuring the impact of anthropogenic climate change. One question is whether we will see “no-go” areas in the future, where the risk is simply too high for insurance and reinsurance companies to take on. As Robert Muir-Wood of RMS explains, there is often a tension between the need for (re)insurers to charge an accurate price for the risk and the political pressure to ensure cover remains available and affordable.

He cites the community at Queen’s Cove in Grand Bahama, where homes were unable to secure insurance given the repeated storm surge flood losses they have sustained over the years from a number of hurricanes. Unable to maintain a mortgage without insurance, properties were left to fall into disrepair. “Natural selection came up with a solution,” says Muir-Wood, whereby some homeowners elevated buildings on concrete stilts thereby making them once again insurable.  

“In high-income, flood-prone countries, such as Holland, there has been sustained investment in excellent flood defenses,” he says. “The challenge in developing countries is there may not be the money or the political will to build adequate flood walls. In a coastal city like Jakarta, Indonesia, where the land is sinking as a result of pumping out the groundwater, it’s a huge challenge. 

“It’s not black and white as to when it becomes untenable to live somewhere. People will find a way of responding to increased incidence of flooding. They may simply move their life up a level, as already happens in Venice, but insurability will be a key factor and accommodating the changes in flood hazard is going to be a shared challenge in coastal areas everywhere.”

Political pressure to maintain affordable catastrophe insurance was a major driver of the U.S. residual market, with state-backed Fair Access to Insurance Requirements (FAIR) plans providing basic property insurance for homes that are highly exposed to natural catastrophes. Examples include the California Earthquake Association, Texas Windstorm Insurance Association and Florida Citizens Property Insurance Corporation (and state reinsurer, the FHCF). 

However, the financial woes experienced by FEMA’s National Flood Insurance Program (NFIP), currently the principal provider of residential flood insurance in the U.S., demonstrates the difficulties such programs face in terms of being sustainable over the long term.  

With the U.K.’s Flood Re scheme, investment in disaster mitigation is a big part of the solution, explains CEO Andy Bord. However, even then he acknowledges that “for some homes at the very greatest risk of flooding, the necessary investment needed to reduce risks and costs would simply be uneconomic.”  

Bringing Clarity to Slab Claims

How will a new collaboration between a major Texas insurer, RMS, Accenture and Texas Tech University provide the ability to determine with accuracy the source of slab claim loss?

The litigation surrounding “slab claims” in the U.S. in the aftermath of a major hurricane has long been an issue within the insurance industry. When nothing is left of a coastal property but the concrete slab on which it was built, how do claims handlers determine whether the damage was predominantly caused by water or wind?

The decision that many insurers take can spark protracted litigation, as was the case following Hurricane Ike, a powerful storm that caused widespread damage across the state after it made landfall over Galveston in September 2008. The storm had a very large footprint for a Category 2 hurricane, with sustained wind speeds of 110 mph and a 22-foot storm surge. Five years on, litigation surrounding how slab claim damage had been wrought rumbled on in the courts.

Recognizing the extent of the issue, major coastal insurers knew they needed to improve their methodologies. It sparked a new collaboration between RMS, a major Texas insurer, Accenture and Texas Tech University (TTU). And from this year, the insurer will be able to utilize RMS data, hurricane modeling methodologies, and software analyses to track the likelihood of slab claims before a tropical cyclone makes landfall and document the post-landfall wind, storm surge and wave impacts over time.

The approach will help determine the source of the property damage with greater accuracy and clarity, reducing the need for litigation post-loss, thus improving the overall claims experience for both the policyholder and insurer. To provide super accurate wind field data, RMS has signed a contract with TTU to expand a network of mobile meteorological stations that are ultimately positioned in areas predicted to experience landfall during a real-time event.

“Our contract is focused on Texas, but they could also be deployed anywhere in the southern and eastern U.S.,” says Michael Young, senior director of product management at RMS. “The rapidly deployable weather stations collect peak and mean wind speed characteristics and transmit via the cell network the wind speeds for inclusion into our tropical cyclone data set. This is in addition to a wide range of other data sources, which this year includes 5,000 new data stations from our partner Earth Networks.”

The storm surge component of this project utilizes the same hydrodynamic storm surge model methodologies embedded within the RMS North Atlantic Hurricane Models to develop an accurate view of the timing, extent and severity of storm surge and wave-driven hazards post-landfall. Similar to the wind field modeling process, this approach will also be informed by ground-truth terrain and observational data, such as high-resolution bathymetry data, tide and stream gauge sensors and high-water marks.

“The whole purpose of our involvement in this project is to help the insurer get those insights into what’s causing the damage,” adds Jeff Waters, senior product manager at RMS. “The first eight hours of the time series at a particular location might involve mostly damaging surge, followed by eight hours of damaging wind and surge. So, we’ll know, for instance, that a lot of that damage that occurred in the first eight hours was probably caused by surge. It’s a very exciting and pretty unique project to be part of.”

Assigning a Return Period to 2017

Hurricanes Harvey, Irma and Maria (HIM) tore through the Caribbean and U.S. in 2017, resulting in insured losses over US$80 billion. Twelve years after Hurricanes Katrina, Rita and Wilma (KRW), EXPOSURE asks if the (re)insurance industry was better prepared for its next ‘terrible trio’ and what lessons can be learned  

In one sense, 2017 was a typical loss year for the insurance industry in that the majority of losses stemmed from the “peak zone” of U.S. hurricanes. However, not since the 2004-05 season had the U.S. witnessed so many landfalling hurricanes. It was the second most costly hurricane season on record for the (re)insurance industry, when losses in 2005 are adjusted for inflation.

According to Aon Benfield, HIM caused total losses over US$220 billion and insured losses over US$80 billion — huge sums in the context of global catastrophe losses for the year of US$344 billion and insured losses of US$134 billion. Overall, weather-related catastrophe losses exceeded 0.4 percent of global GDP in 2017 (based on data from Aon Benfield, Munich Re and the World Bank), the second highest figure since 1990. In that period, only 2005 saw a higher relative catastrophe loss at around 0.5 percent of GDP.

But, it seems, (re)insurers were much better prepared to absorb major losses this time around. Much has changed in the 12 years since Hurricane Katrina breached the levees in New Orleans. Catastrophe modeling as a profession has evolved into exposure management, models and underlying data have improved and there is a much greater appreciation of model uncertainty and assumptions, explains Alan Godfrey, head of exposure management at Asta.

“Even post-2005 people would still see an event occurring, go to the models and pull out a single event ID ... then tell all and sundry this is what we’re going to lose. And that’s an enormous misinterpretation of how the models are supposed to be used. In 2017, people demonstrated a much greater maturity and used the models to advise their own loss estimates, and not the other way around.”

It also helped that the industry was extremely well-capitalized moving into 2017. After a decade of operating through a low interest rate and increasingly competitive environment, (re)insurers had taken a highly disciplined approach to capital management. Gone are the days where a major event sparked a series of run-offs. While some (re)insurers have reported higher losses than others, all have emerged intact.

“In 2017 the industry has performed incredibly well from an operational point of view,” says Godfrey. “There have obviously been challenges from large losses and recovering capital, but those are almost outside of exposure management.”

According to Aon Benfield, global reinsurance capacity grew by 80 percent between 1990 and 2017 (to US$605 billion), against global GDP growth of around 24 percent. The influx of capacity from the capital markets into U.S. property catastrophe reinsurance has also brought about change and innovation, offering new instruments such as catastrophe bonds for transferring extreme risks.

Harvey broke all U.S. records for tropical cyclone-driven rainfall with observed cumulative rainfall of 51 inches

Much of this growth in non-traditional capacity has been facilitated by better data and more sophisticated analytics, along with a healthy appetite for insurance risk from pension funds and other institutional investors.

For insurance-linked securities (ILS), the 2017 North Atlantic hurricane season, Mexico’s earthquakes and California’s wildfires were their first big test. “Some thought that once we had a significant year that capital would leave the market,” says John Huff, president and chief executive of the Association of Bermuda Insurers and Reinsurance (ABIR). “And we didn’t see that.

“In January 2018 we saw that capital being reloaded,” he continues. “There is abundant capital in all parts of the reinsurance market. Deploying that capital with a reasonable rate of return is, of course, the objective.”

Huff thinks the industry performed extremely well in 2017 in spite of the severity of the losses and a few surprises. “I’ve even heard of reinsurers that were ready with claim payments on lower layers before the storm even hit. The modeling and ability to track the weather is getting more sophisticated. We saw some shifting of the storms — Irma was the best example — but reinsurers were tracking that in real time in order to be able to respond.”

The Buffalo Bayou River floods a park in Houston after the arrival of Hurricane Harvey

How Harvey inundated Houston

One lesson the industry has learned over three decades of modeling is that models are approximations of reality. Each event has its own unique characteristics, some of which fall outside of what is anticipated by the models.

The widespread inland flooding that occurred after Hurricane Harvey made landfall on the Texas coastline is an important illustration of this, explains Huff. Even so, he adds, it continued a theme, with flood losses being a major driver of U.S. catastrophe claims for several years now. “What we’re seeing is flood events becoming the No. 1 natural disaster in the U.S. for people who never thought they were at risk of flood.”

Harvey broke all U.S. records for tropical cyclone-driven rainfall with observed cumulative rainfall of 51 inches (129 centimeters). The extreme rainfall generated by Harvey and the unprecedented inland flooding across southeastern Texas and parts of southern Louisiana was unusual.

However, nobody was overly surprised by the fact that losses from Harvey were largely driven by water versus wind. Prior events with significant storm surge-induced flooding, including Hurricane Katrina and 2012’s Superstorm Sandy, had helped to prepare (re)insurers, exposure managers and modelers for this eventuality. “The events themselves were very large but they were well within uncertainty ranges and not disproportionate to expectations,” says Godfrey.

“Harvey is a new data point — and we don’t have that many — so the scientists will look at it and know that any new data point will lead to tweaks,” he continues. “If anything, it will make people spend a bit more time on their calibration for the non-modeled elements of hurricane losses, and some may conclude that big changes are needed to their own adjustments.”

But, he adds: “Nobody is surprised by the fact that flooding post-hurricane causes loss. We know that now. It’s more a case of tweaking and calibrating, which we will be doing for the rest of our lives.”

Flood modeling

Hurricane Harvey also underscored the importance of the investment in sophisticated, probabilistic flood models. RMS ran its U.S. Inland Flood HD Model in real time to estimate expected flood losses. “When Hurricane Harvey happened, we had already simulated losses of that magnitude in our flood model, even before the event occurred,” says Dr. Pete Dailey, vice president of product management and responsible for U.S. flood modeling at RMS.

“The value of the model is to be able to anticipate extreme tail events well before they occur, so that insurance companies can be prepared in advance for the kind of risk they’re taking on and what potential claims volume they may have after a major event,” he adds.

Does this mean that a US$100 billion-plus loss year like 2017 is now a 1-in-6-year event?

Harvey has already offered a wealth of new data that will be fed into the flood model. The emergency shutdown of the Houston metropolitan area prevented RMS meteorologists and engineers from accessing the scene in the immediate aftermath, explains Dailey. However, once on the ground they gathered as much information as they could, observing and recording what had actually happened to affected properties.

“We go to individual properties to assess the damage visually, record the latitude and longitude of the property, the street address, the construction, occupancy and the number of stories,” he says. “We will also make an estimate of the age of the property. Those basic parameters allow us to go back and take a look at what the model would have predicted in terms of damage and loss, as compared to what we observed.”

The fact that insured losses emanating from the flooding were only a fraction of the total economic losses is an inevitable discussion point. The majority of claims paid were for commercial properties, with residential properties falling under the remit of the National Flood Insurance Program (NFIP). Many residential homes were uninsured, however, explains ABIR’s Huff.

“The NFIP covers just the smallest amount of people — there are only five million policies — and yet you see a substantial event like Harvey which is largely uninsured because (re)insurance companies only cover commercial flood in the U.S.,” he says. “After Harvey you’ll see a realization that the private market is very well-equipped to get back into the private flood business, and there’s a national dialogue going on now.”

Is 2017 the new normal?

One question being asked in the aftermath of the 2017 hurricane season is: What is the return period for a loss year like 2017? RMS estimates that, in terms of U.S. and Caribbean industry insured wind, storm surge and flood losses, the 2017 hurricane season corresponds to a return period between 15 and 30 years.

However, losses on the scale of 2017 occur more frequently when considering global perils. Adjusted for inflation, it is seven years since the industry paid out a similar level of catastrophe claims — US$110 billion on the Tohoku earthquake and tsunami, Thai floods and New Zealand earthquake in 2011. Six years prior to that, KRW cost the industry in excess of US$75 billion (well over US$100 billion in today’s money).

So, does this mean that a US$100 billion-plus (or equivalent in inflation-adjusted terms) loss year like 2017 is now a one-in-six-year event? As wealth and insurance penetration grows in developing parts of the world, will we begin to see more loss years like 2011, where catastrophe claims are not necessarily driven by the U.S. or Japan peak zones?

“Increased insurance penetration does mean that on the whole losses will increase, but hopefully this is proportional to the premiums and capital that we are getting in,” says Asta’s Godfray. “The important thing is understanding correlations and how diversification actually works and making sure that is applied within business models.

“In the past, people were able to get away with focusing on the world in a relatively binary fashion,” he continues. “The more people move toward diversified books of business, which is excellent for efficient use of capital, the more important it becomes to understand the correlations between different regions.”

“You could imagine in the future, a (re)insurer making a mistake with a very sophisticated set of catastrophe and actuarial models,” he adds. “They may perfectly take into account all of the non-modeled elements but get the correlations between them all wrong, ending up with another year like 2011 where the losses across the globe are evenly split, affecting them far more than their models had predicted.”

As macro trends including population growth, increasing wealth, climate change and urbanization influence likely losses from natural catastrophes, could this mean a shorter return period for years like last year, where industry losses exceeded US$134 billion?

“When we look at the average value of properties along the U.S. coastline — the Gulf Coast and East Coast — there’s a noticeable trend of increasing value at risk,” says Dailey. “That is because people are building in places that are at risk of wind damage from hurricanes and coastal flooding. And these properties are of a higher value because they are more complex, have a larger square footage and have more stories. Which all leads to a higher total insured value.

“The second trend that we see would be from climate change whereby the storms that produce damage along the coastline may be increasing in frequency and intensity,” he continues. “That’s a more difficult question to get a handle on but there’s a building consensus that while the frequency of hurricane landfalls may not necessarily be increasing, those that do make landfall are increasing in intensity.”

Lloyd’s chief executive Inga Beale has stated her concerns about the impact of climate change, following the market’s £4.5 billion catastrophe claims bill for 2017. “That’s a significant number, more than double 2016; we’re seeing the impact of climate change to a certain extent, particularly on these weather losses, with the rising sea level that impacts and increases the amount of loss,” she said in an interview with Bloomberg.

While a warming climate is expected to have significant implications for the level of losses arising from storms and other severe weather events, it is not yet clear exactly how this will manifest, according to Tom Sabbatelli, senior product manager at RMS. “We know the waters have risen several centimeters in the last couple of decades and we can use catastrophe models to quantify what sort of impact that has on coastal flooding, but it’s also unclear what that necessarily means for tropical cyclone strength.

“The oceans may be warming, but there’s still an ongoing debate about how that translates into cyclone intensity, and that’s been going on for a long time,” he continues. “The reason for that is we just don’t know until we have the benefit of hindsight. We haven’t had a number of major hurricanes in the last few years, so does that mean that the current climate is quiet in the Atlantic? Is 2017 an anomaly or are we going back to more regular severe activity? It’s not until you’re ten or 20 years down the line and you look back that you know for sure.”

Where Tsunami Warnings Are Etched in Stone

As RMS releases its new Japan Earthquake and Tsunami Model, EXPOSURE looks back at the 2011 Tohoku event and other significant events that have shaped scientific knowledge and understanding of earthquake risk 

Hundreds of ancient markers dot the coastline of Japan, some over 600 years old, as a reminder of the danger of tsunami. Today, a new project to construct a 12.5-meter-high seawall stretching nearly 400 kilometers along Japan’s northeast coast is another reminder. Japan is a highly seismically active country and was well prepared for earthquakes and tsunami ahead of the Tohoku Earthquake in 2011. It had strict building codes, protective tsunami barriers, early-warning systems and disaster-response plans.

But it was the sheer magnitude, scale and devastation caused by the Tohoku Earthquake and Tsunami that made it stand out from the many thousands of earthquakes that had come before it in modern times. What had not been foreseen in government planning was that an earthquake of this magnitude could occur, nor that it could produce such a sizable tsunami.

The Tohoku Earthquake was a magnitude 9.0 event — off the charts as far as the Japanese historical record for earthquakes was concerned. A violent change in the ocean bottom triggered an immense tsunami with waves of up to 40 meters that tore across the northeast coast of the main island of Honshu, traveling up to 10 kilometers inland in the Sendai area.

The tsunami breached sea walls and claimed almost everything in its path, taking 16,000 lives (a further 2,000 remain missing, presumed dead) and causing economic losses of US$235 billion. However, while the historical record proved inadequate preparation for the Tohoku event, the geological record shows that events of that magnitude had occurred before records began, explains Mohsen Rahnama, chief risk modeling officer at RMS.

“Since the Tohoku event, there's been a shift ... to moving further back in time using a more full consideration of the geological record” — Mohsen Rahnama, RMS

“If you go back in the geological record to 869 in the Tohoku region, there is evidence for a potentially similarly scaled tsunami,” he explains. “Since the Tohoku event, there’s been a shift in the government assessments moving away from a focus on what happened historically to a more full consideration of the geological record.”

The geological record, which includes tsunami deposits in coastal lakes and across the Sendai and Ishinomaki plains, shows there were large earthquakes and associated tsunami in A.D. 869, 1611 and 1896. The findings of this research point to the importance of having a fully probabilistic tsunami model at a very high resolution.

Rahnama continues: “The Tohoku event really was the ‘perfect’ tsunami hitting the largest exposure concentration at risk to tsunami in Japan. The new RMS tsunami model for Japan includes tsunami events similar to and in a few cases larger than were observed in 2011. Because the exposure in the region is still being rebuilt, the model cannot produce tsunami events with this scale of loss in Tohoku at this time.”

Incorporating secondary perils

In its new Japan earthquake and tsunami model release, RMS has incorporated the lessons from the Tohoku Earthquake and other major earthquakes that have occurred since the last model was released. Crucially, it includes a fully probabilistic tsunami model that is integrated with the earthquake stochastic event set.

“Since the Japan model was last updated we’ve had several large earthquakes around the world, and they all inform how we think about the largest events, particularly how we model the ground motions they produce,” says Ryan Leddy, senior product manager at RMS, “because good instrumentation has only been available over the last several decades. So, the more events where we sample really detailed information about the ground shaking, the better we can quantify it.

“Particularly on understanding strong ground shaking, we utilized information across events,” he continues. “Petrochemical facilities around the world are built with relatively consistent construction practices. This means that examination of the damage experienced by these types of facilities in Chile and Japan can inform our understanding of the performance of these facilities in other parts of the world with similar seismic hazard.”

The Maule Earthquake in Chile in 2010, the Canterbury sequence of earthquakes in New Zealand in 2010 and 2011, and the more recent Kumamoto Earthquakes in Japan in 2016, have added considerably to the data sets. Most notably they have informed scientific understanding of the nature of secondary earthquake perils, including tsunami, fire following earthquake, landslides and liquefaction.

The 2016 Kumamoto Earthquake sequence triggered extensive landsliding. The sequence included five events in the range of magnitude 5.5 to 7.0 and caused severe damage in Kumamoto and Oita Prefectures from ground shaking, landsliding, liquefaction and fire following earthquake.

“Liquefaction is in the model as a secondary peril. RMS has redesigned and recalibrated the liquefaction model for Japan. The new model directly calculates damage due to vertical deformation due to liquefaction processes,” says Chesley Williams, senior director, RMS Model Product Management. “While the 1964 Niigata Earthquake with its tipped apartment buildings showed that liquefaction damages can be severe in Japan, on a countrywide basis the earthquake risk is driven by the shaking, tsunami and fire following, followed by liquefaction and landslide. For individual exposures, the key driver of the earthquake risk is very site specific, highlighting the importance of high-resolution modeling in Japan.”

The new RMS model accounts for the clustering of large events on the Nankai Trough. This is an important advancement as an examination of the historical record shows that events on the Nankai Trough have either occurred as full rupturing events (e.g., 1707 Hoei Earthquake) or as pairs of events (e.g., 1944 and 1946 and two events in 1854).

This is different from aftershocks, explains Williams. “Clustered events are events on different sources that would have happened in the long-term earthquake record, and the occurrence of one event impacts the timing of the other events. This is a subtle but important distinction. We can model event clustering on the Nankai Trough due to the comprehensive event record informed by both historical events and the geologic record.”

The Tohoku event resulted in insurance losses of US$30 billion to US$40 billion, the costliest earthquake event for the insurance industry in history. While the news media focused on the extreme tsunami, the largest proportion of the insurance claims emanated from damage wrought by the strong ground shaking. Interestingly, likely due to cultural constraints, only a relatively low amount of post-event loss amplification was observed.

“In general for very large catastrophes, claims costs can exceed the normal cost of settlement due to a unique set of economic, social and operational factors,” says Williams. “Materials and labor become more expensive and claims leakage can be more of an issue, so there are a number of factors that kick in that are now captured by the RMS post-event loss amplification modeling. The new Japan model now explicitly models post-event loss amplification but limits the impacts to be consistent with the observations in recent events in Japan.”

Supply chain disruption and contingent business interruption were significant sources of loss following the Tohoku event. This was exacerbated by the level seven meltdown at the Fukushima nuclear power plant that resulted in evacuations, exclusion zones and rolling blackouts.

“We sent reconnaissance teams to Japan after the event to understand the characteristics of damage and to undertake case studies for business interruption,” says Williams. “We visited large industrial facilities and talked to them about their downtime, their material requirement and their access to energy sources to better understand what had impacted their ability to get back up and running.”

Recent events have re-emphasized that there are significant differences in business interruption by occupancy. “For example,  a semiconductor facility is likely going to have a longer downtime than a cement factory,” says Williams. “The recent events have highlighted the impacts on business interruption for certain occupancies by damage to supply sources. These contingent business interruptions are complex, so examination of the case studies investigated in Japan were instrumental for informing the model.”

Rebuilding in the seven years since the Tohoku Tsunami struck has been an exercise in resilient infrastructure. With nearly half a million people left homeless, there has been intense rebuilding to restore services, industry and residential property. US$12 billion has been spent on seawalls alone, replacing the 4-meter breakwaters with 12.5-meter-high tsunami barriers.

An endless convoy of trucks has been moving topsoil from the hills to the coastline in order to raise the land by over 10 meters in places. Most cities have decided to elevate by several meters, with a focus on rebuilding commercial premises in exposed areas. Some towns have forbidden the construction of homes in flat areas nearest the coasts and relocated residents to higher ground.

Tokyo-Yokohama: The world's most exposed metropolis

The Japanese metropolis of Tokyo-Yokohama has the world's greatest GDP at risk from natural catastrophes. Home to 38 million residents, it has potential for significant economic losses from multiple perils, but particularly earthquakes. According to Swiss Re it is the riskiest metropolitan area in the world.

A combination of strict building codes, land use plans and disaster preparedness have significantly reduced the city's vulnerability in recent decades. Despite the devastation caused by the tsunami, very few casualties (around 100) related to partial or complete building collapse resulting from ground shaking during the magnitude 9.0 Tohoku Earthquake.