Launching a New Journal for Terrorism and Cyber Insurance

Natural hazard science is commonly studied at college, and to some level in the insurance industry’s further education and training courses. But this is not the case with terrorism risk. Even if insurance professionals learn about terrorism in the course of their daily business, as they move into other positions, their successors may begin with hardly any technical familiarity with terrorism risk. It is not surprising therefore that, even fifteen years after 9/11, knowledge and understanding of terrorism insurance risk modeling across the industry is still relatively low.

There is no shortage of literature on terrorism, but much has a qualitative geopolitical and international relations focus, and little is directly relevant to terrorism insurance underwriting or risk management.

As a step towards redressing the imbalance in available terrorism literature, a new online journal, The Journal of Terrorism and Cyber Insurance, has been established; its launch is to coincide with the fifteenth anniversary of 9/11. The journal has been welcomed and supported by global terrorism insurance pools, and its launch will be publicized at the annual terrorism pools congress in Canberra, Australia, on October 7, 2016.

Originally conceived as a journal of terrorism insurance, coverage has been extended to include cyber risk, recognizing the increasing insurance industry concerns over cyber terrorism and the burgeoning insurance market in cyber risk. The aim of the open access journal is to raise the industry’s level of knowledge and understanding of terrorism risk. By increasing information transparency for this subject the editorial board hopes to facilitate the growth of the terrorism insurance market, which serves the risk management requirements of the wider international community. The first issue is a solid step in this direction, and will include articles on the ISIS attacks in Paris in November 2015; terrorism insurance in France and Australia; parametric terrorism insurance triggers; non-conventional threats; the clean-up costs of anthrax, and the terrorist use of drones.

The four founding editors of the journal have extensive knowledge of the field. The managing editor is Rachel Anne Carter, who has terrorism insurance administrative experience with both OECD and U.K. Pool Re. Dr. Raveem Ismail, specialty terrorism underwriter at Ariel Re, brings to the editorial board detailed direct terrorism and political risk underwriting knowledge. Padraig Belton is a writer with extensive political risk expertise, having served as a correspondent in the Middle East and Pakistan. As chief architect of the RMS terrorism model, I will bring terrorism risk modeling expertise to the team and have the responsibility and pleasure to review all article submissions. I look forward to sharing insights from the journal with subscribers to this blog.

No More Guessing Games for Marine Insurers

Huge ports mean huge amounts of cargo. Huge amounts of cargo mean huge accumulations of risk.

As a guiding principle about where marine insurers are exposed to the highest potential losses, it seems reasonable enough. But in fact, as RMS research has proven this week, this proposition may be a bit misleading. Surprisingly, a port’s size and its catastrophe loss potential are not strongly correlated.

Take the Port of Plaquemines, LA which is just south-east of New Orleans. It is neither well known nor big in comparison with others around the world. Yet it has the third highest risk in the world of insurance loss due to catastrophe: our analysis revealed its 500-year marine cargo loss from hurricane would be $1.5 billion.

Plaquemines is not an isolated case. There were other smaller ports in our ranking: Pascagoula, MS in the United States ranks 6 on our list with a potential $1 billion marine cargo loss due to storm surge and hurricane; Bremerhaven in Germany (ranked 4th at $1 billion) and Le Havre in France (ranked 10th at $0.7 billion).

Asia-Pacific ports featured less frequently, but worryingly one Asia port topped the list: Nagoya, Japan was number 1 ($2.3 billion potential losses) with Guangzhou, China a close second ($2 billion). Our analysis modeled risk posed by earthquake, wind, and storm surge perils in a 500-year return period across 150 ports – the top ten results are further down this blog.

Ports At Risk For Highest Lost
(500 year estimated catastrophe loss for earthquake, wind, and storm surge perils)

Estimated Marine Cargo Loss in Billions USD
1 Nagoya, Japan 2.3
2 Guangzhou, China 2.0
3 Plaquemines, LA, U.S. 1.5
4 Bremerhaven, Germany 1.0
5 New Orleans, LA, U.S. 1.0
6 Pascagoula, MS, U.S. 1.0
7 Beaumont, TX, U.S. 0.9
8 Baton Rouge, LA, U.S. 0.8
9 Houston, TX, U.S. 0.8
10 Le Havre, France 0.7

* Losses rounded to one decimal place.

Our analysis demonstrates what we at RMS have long suspected: outdated marine risk modeling tools and incomplete data obscure many high-risk locations, big and small. These ports are risky because of the natural perils they face and the cargos which transit through them, as well as the precise way those cargos are stored. But many in the marine sector don’t have these comprehensive insights. Instead they have to make do with a guessing game in determining catastrophe risk and port accumulations. And with the advanced analytics available in 2016 this is no longer good enough.

Big Port or Small – Risk Can Now Be Determined

Back to that seemingly simple proposition about the relationship between port size and insurance risk which I began this blog with. As the table above demonstrates, smaller ports can also present a huge risk.

But the bigger ships and bigger ports brought about by containerization have led, overall, to a bigger risk exposure for marine insurers. Not least because larger vessels have rendered many river ports inaccessible forcing shippers to rely on seaside ports, which are more vulnerable to hurricanes, typhoons, and storm surge.

The value of global catastrophe-exposed cargo is already huge and is likely to keep growing. But the right tools, which use the most precise data, can reveal where the risk of insurance loss is greatest. Leveraging these tools, (re)insurers can avoid dangerous cargo accumulations and underwrite with greater confidence.

Which means that, at last, the guessing game can stop.

In a box: Our ranking of high risk ports used the new RMS Marine Cargo Model™, with geospatial analysis of thousands of square kilometers of satellite imagery across ports in 43 countries. RMS’ exposure development team used a proprietary technique for allocating risk exposure across large, complex terminals to assess the ports’ exposure and highlight the risk of port aggregations. The model took into account:

  • Cargo type (e.g. autos, bulk grains, electronics, specie)
  • Precise storage location (e.g. coastal, estuarine, waterside or within dock complex)
  • Storage type (e.g. open air, warehouse, container — stacked or ground level)
  • Dwell time (which can vary due to port automation, labor relations and import/export ratios)

Insurance-Linked Securities in Asia – Looking Out for the Tipping Point

We were at a conference in Singapore, pushing to develop a market that doesn’t yet really exist. Grounds, you might think, for frustration.

And yet my RMS capital markets colleague, Jin Shah, and I were upbeat and, in truth, a little excited.

So often we end up at ILS conferences talking to the same audiences about the same topics. But this was different. The inaugural ILS Asia Conference organized by Artemis.bm, the de facto bulletin-board for the ILS industry, had 170 industry experts and practitioners from the region gathered in the Raffles Hotel ballroom.

The aim of the event was to demonstrate the ILS industry’s commitment to building a global footprint and developing expertise in the asset class among Asia’s investors and reinsurers. This conference was exciting because we can see the Asia insurance industry will approach a tipping point in the next decade or so, resulting in increased appetite in Asian ILS instruments from both sides. Let me explain how.

An Insurance Market Which Has Not Yet Matured

Currently in many Asian countries, the insurance market is still developing and the concept of insurance as a social and economic “good” is still not culturally normalized. In addition, mandatory insurance outside of auto/motor is, in some places, almost non-existent, with individuals looking instinctively to family and other social networks to provide financial safety-net.

Because of these factors, combined with generally lower levels of disposable income, property insurance penetration, in particular, is comparatively low in Asia. Thus, the region only contributes a small amount to reinsurer’s portfolios and capital loads. So they don’t yet need to transfer some of that risk to the capital markets as is the case in core, concentrated regions such as the U.S., Japan, and Europe. The economics of ILS in Asia are challenging to say the least, and in some cases, make fully collateralized products “non-starters” from a competitive point of view.

Growing Populations and Changing Demographics

But that’s the current environment. The future growth of the middle classes, particularly in China and India, will fuel increasing demand for all forms of insurance as more people chose to protect their assets against damage and loss. Given the sheer size of the population and their rate of growth, it is not inconceivable that within ten years these markets could represent a similar level of risk concentration to (re)insurers as the U.S., Europe, or Japan.

And that’s the tipping point.

In certain Asian countries, the ILS sector is already developed. For a number of years, Australian insurers have been tapping the capital markets as a strategic element of their outwards protection. Japanese risk has been a core part of the risk available in both the cat bond and collateralized re markets. Outside of these more mature markets, last year China Re issued their Panda Re cat bond which, whilst only being a $50 million dip-of-a-toe in the water, showed that ILS funds were keen to accept China risk and pave the way for larger issuances in the future.

And with social, demographic and economic changes in the years ahead Asia will provide a natural hunting ground for ILS funds, keen to leverage their broad and diversified capital base to support the local insurance market’s continued growth.

Sensing this future tipping point too, the Artemis conference was attended by more than 25 industry stalwarts who’d travelled from London, Bermuda, New York, San Francisco, Japan, and Australia to bring the conversation to new audiences. ILS investors are clearly looking to this region to diversify their own portfolios, both as a risk management measure and with an eye to the rapid growth occurring in the region – and the opportunities it presents.

Searching for Clues After the Ecuador Earthquake

Reconnaissance work is built into the earthquake modeler’s job description – the backpack is always packed and ready. Large earthquakes are thankfully infrequent, but when they do occur, there is much to be learned from studying their impact, and this knowledge helps to improve risk models.

An RMS reconnaissance team recently visited Ecuador. Close to 7pm local time, on April 16, 2016, an Mw7.8 earthquake struck between the small towns of Muisne and Pedernales on the northwestern coast of Ecuador. Two smaller, more recent earthquakes have also impacted the area, on July 11, 2016 an Mw5.8 and Mw6.2, fortunately with no significant damage.

April’s earthquake was the strongest recorded in the country since 1979 and, at the time of writing, the strongest earthquake experienced globally so far in 2016. The earthquake caused more than 650 fatalities, more than 17,600 injuries, and damage to more than 10,000 buildings.

Two weeks after the earthquake, an RMS reconnaissance team of engineers started their work, visiting five cities across the affected region, including Guayaquil, Manta, Bahía de Caráquez, Pedernales, and Portoviejo. Pedernales was the most affected, experiencing the highest damage levels due to its proximity to the epicenter, approximately 40km to the north of the city.

Sharing the Same Common Vulnerability

The majority of buildings in the affected region were constructed using the same structural system: reinforced concrete (RC) frames with unreinforced concrete masonry (URM) infill. This type of structural system relies on RC beams and columns to resist earthquake shaking, with the walls filled in with unreinforced masonry blocks. This system was common across residential, industrial, and commercial properties and across occupancies, from hospitals and office buildings to government buildings and high-rise condominiums.

URM infill is particularly susceptible to damage during earthquakes, and for this reason it is prohibited by many countries with high seismic hazard. But even though Ecuador’s building code was updated in 2015, URM infill walls are still permitted in construction, and are even used in high-end residential and commercial properties.

Without reinforcing steel or adequate connection to the surrounding frame, the URM often cracks and crumbles during strong earthquake shaking. In some cases, damaged URM on the exterior of buildings falls outward, posing safety risks to people below. And for URM that falls inward, besides posing a safety risk, it often causes damage to interior finishes, mechanical equipment, and contents.

Across the five cities, the observed damage ranged from Modified Mercalli Intensity (MMI) 7.0-9.0. For an MMI of 7.0, the damage equated to light to moderate damage of URM infill walls, and mostly minimal damage to RC frames with isolated instances of moderate-to-heavy damage or collapse. An MMI of 9.0, which based on RMS observations, occurred in limited areas, meant moderate to heavy damage of URM infill walls and slight to severe damage or collapse to RC frames.

While failure of URM infill was the most common damage pattern observed, there were instances of partial and even complete structural collapse. Collapse was often caused, at least in part by poor construction materials and building configurations, such as vertical irregularities, that concentrated damage in particular areas of buildings.

Disruption to Business and Public Services

The RMS team also examined disruption to business and public services caused by the earthquake. A school in Portoviejo will likely be out of service for more than six months, and a police station in Pedernales will likely require more than a year of repair work. The disruption observed by the RMS team was principally due to direct damage to buildings and contents. However, there was some disruption to lifeline utilities such as electricity and water in the affected region, and this undoubtedly impacted some businesses.

RMS engineers also visited four public hospitals and clinics, with damage ranging from light to complete collapse. The entire second floor of a clinic in Portoviejo collapsed. A staff doctor told RMS that the floor was empty at the time and all occupants, including patients, evacuated safely.

Tourism was disrupted, with a few hotels experiencing partial or complete collapse. In some cases, even lightly damaged and unaffected hotels were closed as they were within cordoned-off zones in Manta or Portoviejo.

Tuna is an important export product for Ecuador. Two plants visited sustained minor structural damage, with unanchored machinery requiring repositioning and recalibration. One tuna processing plant reached 100% capacity just 16 days after the earthquake. Another in Manta reached 85% capacity about 17 days after the earthquake, and full capacity was expected within one month.

The need for risk differentiation

Occupancy, construction class, year built, and other building characteristics influence the vulnerability of buildings and, consequently, the damage they sustain during earthquakes. Vulnerability is so important in calculating damage from earthquakes that RMS model developers go to great lengths to ensure that each country’s particular engineering and construction practices are accurately captured by the models. This approach enables the models to differentiate risk across thousands of different factors.

Residential insurance penetration in Ecuador is still relatively low for commercial buildings and privately owned or financed homes, but higher amongst government-backed mortgages, as these require insurance. The knowledge gained from reconnaissance work is fundamental to our understanding of earthquake risk and informs future updates to RMS models. Better models will improve the insurance industry’s understanding and management of earthquake risk as insurance penetration increases both here and around the world.

How U.S. inland flood became a “peak” peril

This article by Jeff Waters, meteorologist and product manager at RMS, first appeared in Carrier Management.

As the journey towards a private flood insurance market progresses, (re)insurers can learn a lot from the recent U.S. flood events to help develop profitable flood risk management strategies.

Flood is the most pervasive and frequent peril in the U.S. Yet, despite having the world’s highest non-life premium volume and one of the highest insurance penetration rates, a significant protection gap still exists in the U.S. for this peril.

It is well-known that U.S. flood risk is primarily driven by tropical cyclone-related events, with storm surge being the main cause. In the last decade alone, flooding from tropical cyclones have caused more than $40 billion (2015 USD) in insured losses and contributed to today’s massive $23 billion National Flood Insurance Program (NFIP) deficit: 13 out of the top 15 flood events, determined by total NFIP payouts, were related to storm surge-driven coastal flooding from tropical cyclones.

Inland flooding, however, should not be overlooked. It too can contribute to a material portion of overall U.S. flood risk, as seen recently in the Southern Gulf, South Carolina, and in West Virginia, two areas impacted by major loss-causing events. These catastrophes caused billions in economic and insured losses while demonstrating the widespread impact caused by precipitation-driven fluvial (riverine) or pluvial (surface water) flooding. It is these types of flooding events that should be accounted for and well understood by (re)insurers looking to enter the private flood insurance market.

It hasn’t just rained; it has poured

In the past 15 months the U.S. has suffered several record-breaking or significant rainfall-induced inland flood events ….

To read the article in full, please click here.

A Perennial Debate: Disaster Planning versus Disaster Response

In May we saw a historic first: the World Humanitarian Summit. Held in Istanbul, representatives of 177 states attended. One UN chief summarised its mission thus: “a once-in-a-generation opportunity to set in motion an ambitious and far-reaching agenda to change the way that we alleviate, and most importantly prevent, the suffering of the world’s most vulnerable people.”

And in that sentence we find one of the enduring tensions within the disaster field: between “prevention” and “alleviation.” Between on the one hand reducing disaster risk through resilience-building investments, and on the other reducing suffering and loss through emergency response.

But in a world of constrained political budgets, where should we concentrate our energies and resources: disaster risk reduction or disaster response?

How to Close the Resilience Gap

The Istanbul summit saw a new global network launched to engage business in crisis situations through “pre-positioning supplies, meeting humanitarian needs and providing resources, knowledge and expertise to disaster prevention.” It is, of course, prudent to have stockpiles of humanitarian supplies strategically placed.

But is the dialogue still too focused on response? Could we not have hoped to see a greater emphasis on driving the disaster-resilient behaviours and investments, which reduce the reliance on emergency response in the first place?

Politics & Priorities

“Cost-effectiveness” is a concept with which humanitarian aid and governmental agencies have struggled over many years. But when it comes to building resilience, it is in fact possible to cost-justify the best course of action. After all, the insurance industry, piqued by the dual surprise of Hurricane Andrew and then the Northridge earthquake, has been using stochastic models to quantify and reduce catastrophe risk since the mid-1990s.

Unfortunately risk/reward analyses are rarely straightforward in practice. This is less a failing of the models to accurately characterise complex phenomena, though that certainly is a challenge. It’s more a question of politics.

It is harder for any government to argue that spending scarce public funds on building resilience in advance of a possible disaster is money well spent. By contrast, when disaster strikes and human suffering is writ large across the media, then there is a pressing political imperative to intervene. As a result many agencies sadly allocate more funds to disaster response than to disaster prevention, even though the analytics mostly suggest the opposite would be more beneficial.

A New, Ambitious form of Public Private Partnership

But there are signs that across the different strata of government the mood is changing. The cities of San Francisco and Berkeley, for example, have begun to use catastrophe models to quantify the cost of inaction and thereby drive risk-reducing investments. For San Francisco the focus has been on protecting the city’s economic and social wealth from future sea level rise. In Berkeley, resilience models have been deployed to shore-up critical infrastructure against the threat of earthquakes.

In May, RMS held the first international workshop on how resilience analytics can be used to manage urban resilience. Attended by public officials from several continents the engagement in the topic was very high.

The role of resilience analytics to help design, implement, and measure resilience strategies was emphasized by Arnoldo Kramer, the first Chief Resilience Officer (CRO) of the largest city in the western hemisphere, Mexico City. The workshop discussion went further than just explaining how these models can be used to quantify the potential, risk-adjusted return on investment from resilience initiatives. The group stressed the role of resilience metrics in helping cities finance capital investments in new, protective infrastructure.

Stimulated by commitments under the Sendai Framework to work more closely with the private sector, lower income regions are also increasingly benefiting from such techniques – not just to inform disaster response, but also to finance the reduction of disaster risk in the first place. Indeed there are encouraging signs that these two different worlds are beginning to understand each other better. At the inaugural working group meeting of the Insurance Development Forum in Singapore last month there was a productive dialogue between the UN Development Programme and the risk transfer industry. It was clear that both sides wanted action, not just words.

Such initiatives can only serve to accelerate the incorporation of resilience analytics into existing disaster risk reduction programmes. This may be a once-in-a-generation opportunity to address the shameful gap between the economic costs of natural disasters and the fraction of those costs that are insured.

We cannot prevent natural disasters from happening. But neither can we continue to afford to spend billions of dollars picking up the pieces when they strike. I am hopeful that we will take this opportunity to bring resilience analytics into under-served societies, making them tougher, more resilient, so that when catastrophe strikes, the impact is lessened and societies can bounce back far more readily.

Using Insurance Claims Data to Drive Resilience

When disaster strikes for homeowners and businesses the insurance industry is a source of funds to pick up the pieces and carry on. In that way the industry provides an immediate benefit to society. But can insurers play an extended role in helping to reduce the risks for which they provide cover, to make society more resilient to the next disaster?

Insurers collect far more detailed and precise information on property damage than any other public sector or private organisation. Such claims data can provide deep insights into what determines damage – whether it’s the vulnerability of a particular building type or the fine scale structure of flood hazard.

While the data derived from claims experience helps insurers to price and manage their risk, it has not been possible to apply this data to reduce the potential for damage itself – but that is changing.

At a recent Organisation for Economic Co-operation and Development meeting in Paris on flood risk insurance we discussed new initiatives in Norway, France and Australia that harness and apply insurers’ claims experience to inform urban resilience strategies.

Norway Claims Data Improves Flood Risk

In Norway the costs of catastrophes are pooled across private insurance companies, making it the norm for insurers to share their claims data with the Natural Perils Pool. Norwegian insurers have collaborated to make the sharing process more efficient, agreeing a standardized approach in 2008 to address-level exposure and claims classifications covering all private, commercial and public buildings. Once the classifications were consistent it became clear that almost 70% of flood claims were driven by urban flooding from heavy rainfall.

Starting with a pilot of ten municipalities, including the capital Oslo, a group funded by the Norwegian finance and insurance sector took this address-level data to the city authorities to show exactly where losses were concentrated, so that the city engineer could identify and implement remedial actions: whether through larger storm drains or flood walls. As a result flood claims are being reduced.

French Observatory Applies Lessons Learned from Claims Data

Another example is from France, where natural catastrophe losses are refunded through the national ‘Cat Nat System’. Property insureds pay an extra 12% premium to be covered. All the claims data generated in this process now gets passed to the national Observatory of Natural Risks, set up after Storm Xynthia in 2010. This unit employs the data to perform forensic investigations to identify what can be learnt about the claims and then works with municipalities to see how to apply these lessons to reduce future losses. The French claims experience is not as comprehensive as in Norway because such data only gets collected when the state declares there has been a ‘Cat Nat event’  – which excludes some of the smaller and local losses that fail to reach the threshold of political attention.

Australian Insurers Forced Council to Act on Their Claims Data

In Australia sharing claims data with a city council was the result of a provocative action by insurers which were frustrated by the political pressure to offer universal flood insurance following the major floods in 2011.  Roma, a town in Queensland, had been inundated five times in six years – insurers mapped and published the addresses of the properties that had been repeatedly flooded and refused to renew the insurance cover unless action was taken. The insurers’ campaign achieved its goal, pressuring the local council to fund flood alleviation measures across the town.

These examples highlight how insurers can help cities identify where their investments will accomplish the most cost-effective risk reduction. All that’s needed is an appetite to find ways to process and deliver claims data in a format that provides the key insights that city bosses need, without compromising concerns around confidentiality or privacy.

This is another exciting application in the burgeoning new field of resilience analytics.

The Rising Cost of Hurricanes – and America’s Ability to Pay

Future hurricanes are going to cost the U.S. more money and, if we don’t act to address this, it will leave the government struggling to cope. That is the finding of a recent Congressional Budget Office (CBO) report which put it starkly:

“…over time, the costs associated with hurricane damage will increase more rapidly than the economy will grow. Consequently, hurricane damage will rise as a share of gross domestic product (GDP)…”

The CBO identified two core drivers for the escalating costs: climate change, which will drive just under half of the potential increases in hurricane damages while just over half of damages will come from coastal development. The four main four variables that would have the most impact were identified as:

  • Changes in sea levels for different U.S. states;
  • changes in the frequency of hurricanes of various intensities;
  • population growth in coastal areas, and;
  • per capita income in coastal areas.

Using Catastrophe Models to Calculate the Future Cost of Hurricanes

To inform the CBO’s research and creation of a range of possible hurricane scenarios based on future changes to the four key variables, RMS hurricane and storm surge risk experts provided the CBO with data from the RMS North Atlantic Hurricane Model and Storm Surge Model.

Through RMS’ previous work with the Risky Business Initiative we were able to provide state specific “damage functions” which were used to translate possible future hurricane events, state-specific sea levels and current property exposure into expected damaged. While we usually produce loss estimates for catastrophes, we didn’t provide the CBO with estimated losses ourselves – rather we built a tool so the CBO could “own” its own assumptions about changes in all the factors – a critical aspect of the CBO’s need to remain impartial and objective.

Solutions to Increase Coastal Urban Resilience

The CBO’s report includes suggested policies that could decrease the pressure on federal spending. The polices range from global initiatives to limit greenhouse gas emissions to more direct mechanisms that could shift costs to state and local governments and private entities, as well as investing in structural changes to reduce vulnerabilities. Such approaches bring to the forefront the role of local resilience in tackling a global problem.

However, therein lies the challenge. Many of the options open to society to increase resiliency against catastrophes, could have a less positive effect on the economy. It’s an issue that has been central to the wider debate about reducing the impacts of climate change. Limiting greenhouse gas emissions has direct effects on the oil and gas industry.  Likewise, curbing coastal development impacts developers and local economies. It has led states such as North Carolina to ban the use of future sea level rise projections as the basis for policies on coastal development.

Overcoming Political Resistance

Creating resiliency in U.S. towns and communities needs to be a multi-faceted effort. While initiatives to fortify the building stock and curb global climate change and sea level rise are moving ahead there is strong resistance from the political arena.  To overcome the resistance, solutions to transition the economy to new forms of energy must be found, as well as ways to adapt the current workforce to the jobs of the future. City leaders and developers should partner to find sustainable growth initiatives for urban growth, to ease the fears that coastal cities will wither and die under new coastal use restrictions.

Initiating these conversations will go a long way to removing the barriers to success in curbing greenhouse gas emissions and limiting coastal growth. With an already polarised political debate on climate change this CBO report may provoke further controversy about how to deal with the factors behind the increase in future hurricane damage costs. Though one conclusion is inescapable: catastrophe losses are going up and we will all be footing the bill.

This post was co-authored by Paul Wilson and Matthew Nielsen.

Matthew Nielsen

Senior Director of Global Governmental and Regulatory Affairs, RMS

Matthew Nielsen leads Governmental and Regulatory Affairs. He is responsible for maintaining relationships with regulators, legislators, and rating agencies on behalf of the company to establish open channels of communication around RMS models and solutions. Matthew is a meteorologist and geographer with extensive experience in North American catastrophe risk. In his prior role at RMS, he was responsible for developing the RMS climate peril models for the Americas, including the severe convective storm, winter storm, flood, and hurricane models. He has conducted field reconnaissance for major catastrophes including Hurricanes Katrina and Sandy. Before joining RMS, Matthew conducted remote sensing in satellite meteorology research at the Cooperative Institute for Research in the Atmosphere (CIRA). He holds a BS in physics from Ripon College, where he won the Henry Knop Award in Physics, and an MS in atmospheric science from Colorado State University. Matthew is a member of the American Meteorological Society (AMS), the International Society of Catastrophe Managers (ISCM), and the American Association of Geographers (AAG).

Euro 2016: France inundated by fans and floods

This week the final knockout rounds of Euro 2016 take place in France. Sadly, England has long since left the country and the tournament, largely due to some inept displays. But more miserable than England’s performance, was the weather at the start of the tournament, which caused concern in the capital as intense precipitation on top of an already saturated France, led to severe flooding.

Some areas of the country experienced the worst flooding they have seen in a century, with the floods across eastern and central France declared a natural disaster by French President François Hollande. River levels in the Seine were at their highest in nearly 35 years, impacting Paris, and leading to three of the capital’s best-known museums — the Louvre, the Grand Palais, and Orsay —closing their doors to the public, as staff moved priceless works of art to the safety of higher floors.

Source: The Guardian

There were also concerns surrounding how the flooding could impact the tournament. However, as you can see in the below image, which represents the RMS 1,000 year inland flood hazard extent, neither of the two stadia located in France’s capital (yellow markers) were really at any risk of flooding. The same can’t be said for the fan zone adjacent to the Eiffel Tower though (red marker). Continued intense rainfall, would have led to increased flood severity, meaning that 90,000 or so fans would have been in need of their waders.

Stade de France and Parc des Princes (yellow markers); Paris Fan Zone (red marker)

Paris wasn’t the only location in France to be impacted by the floods though; further south the town of Nemours observed severe flooding as the River Loing burst its banks. While devastating to the local community, this severity of flooding can be expected in the town. The RMS Europe Inland Flood maps demonstrate such flooding for events in excess of the 50 year return period, but as the below image of the 200 year flood extent demonstrates, the flooding could have been even more severe.

Rue de Paris, Nemours (yellow marker) and Château-Musée de Nemours (red marker)

The flooding in Nemours is a good example of why it is so important to understand the standard of protection offered by local flood defenses, in order to fully understand flood risk. The RMS Europe Inland Flood models and maps explicitly represent the impact of flood defenses and provide some noteworthy insights into the potential exposure at risk, if the standard of protection is not maintained or local flood defenses are overtopped.

Rue de Paris, Nemours. Source: The Guardian

If we removed all flood defenses and consider a 100 year return period level of flood hazard across France, the RMS analyses estimate that over €600 billion of insured exposure is at risk to flood damage. However, approximately 40 percent of this exposure at risk is protected against such levels of hazard by local flood defenses.

Source: Château-Musée de Nemours

And in the largest exposure concentrations, such as Paris and its surrounding area, the importance of local defenses is even more prominent. Looking at a similar 100 year return period level of flood hazard in this region, almost €60 billion of insured exposure would be at risk of flooding, but approximately 90 percent of that exposure is protected against this level of hazard.

Flood can be thought of as a polar peril; if you’re in the extent of a flood event, the costs are high but if you’re on the edge then you’re safe. And for this reason, an understanding of the impact of flood defenses is vital, because if they breach or become overtopped, the losses can be high. Knowing where exposure is protected allows you to write business smartly in higher risk zones. But understanding the hazard, should defenses fail, is also vital, enabling a more informed understanding of severe flood risk and its associated uncertainties.

This post was co-authored by Rachael Whitford and Adrian Mark.

Cat Losses, The Atlantic Basin, & Technology

Technology can be a powerful ally in the battle to successfully assess and manage risk. From new, high-definition models to fully hosted solutions that shrink costs and timeframes, risk professionals now have access to the tools they need to successfully manage their portfolios.

Advances both in the collection of data and computational strength have enabled more precise and comprehensive analytics than were previously possible, thus allowing a more complete and accurate risk profile.

The more you know about risk and exposure, the more they can be managed. Unmanaged or undermanaged, risks, and exposures can become problems and even turn tragic or fatal.

Global insured losses from catastrophes totaled $37 billion in 2015 according to Swiss Re’s most recent Sigma Study. The 2015 figure, at just over half the inflation-adjusted previous 10-year average of $62 billion in insured catastrophe losses, was substantially tied to a quiet Atlantic hurricane season.

“The relatively low level of losses was largely due to another benign hurricane season in the US. El Niño in 2015 contributed to weather patterns deviating from average climate norms,” said the Swiss Re report.

(Re)insurers’ financial results for the past two years have been dotted with the phrase “benign catastrophe losses,” demonstrating how they have benefitted from quiet Atlantic storm conditions producing below-average claims activity.

That period of below-average catastrophe losses for (re)insurers may be coming to an end as researchers and forecasters are pointing toward a more active Atlantic hurricane season for 2016.

When (not if) catastrophe losses do return to their 10-year average, that’s $25 billion across somebody’s balance sheet. What might the 2016 Atlantic hurricane season hold for the U.S. and those who insure it?

With ports lining the U.S. coast from Texas to New York, even one landfall could wreak havoc on marine activities and infrastructure as the country moves into the winter holiday and heating oil seasons.

More Active Season?

While 2015 saw only 11 named storms with just four hurricanes, early indications suggest that the 2016 season will exceed those totals.

An April 14 update from the Climate Prediction Center of the National Oceanic and Atmospheric Administration (NOAA) said that the current El Nino conditions, known to inhibit hurricane activity, are likely to abate.

El Niño is dissipating and NOAA’s Climate Prediction Center is forecasting a 70 percent chance that La Niña—which favors more hurricane activity—will be present during the peak months of hurricane season, August through October.

“Nearly all models predict further weakening of El Niño, with a transition to ENSO-neutral likely during late spring or early summer 2016. Then, the chance of La Niña increases during the late summer or early fall,” the Center said in its update.

The Colorado State University Tropical Meteorology Project issued a forecast that included an estimated 12 named storms and five hurricanes, again greater than observed 2015 totals.

The Weather Company’s Professional Division issued a report stating the 2016 Atlantic Hurricane season would be he most active since 2012. This report forecasts 14 named storms, eight hurricanes, and three major hurricanes, more than the 30-year historical average of 12 named storms, six hurricanes, and three major hurricanes, according to The Weather Channel.

Most recently, NOAA followed its earlier report on El Nino with its annual Atlantic Hurricane Forecast, stating that this year’s hurricane season will see closer to Normal activity after three slow years.

“A near-normal prediction for this season suggests we could see more hurricane activity than we’ve seen in the last three years, which were below normal,” said Gerry Bell, Ph.D., lead seasonal hurricane forecaster with NOAA’s Climate Prediction Center.

The NOAA forecast predicts a 70% likelihood of 10 to 16 named storms, of which 4 to 8 could become hurricanes and 1 to 4 major hurricanes (Category 3, 4, or 5). In addition to a near-normal season being most likely with a 45% chance, there is also a 30% chance of an above-normal season and a 25% chance of a below-normal season.

Another ominous harbinger was the formation of tropical storm Colin on June 5—the earliest third storm on record in the Atlantic basin. Colin then made landfall on June 6 along Florida’s Big Bend with maximum sustained winds of 50 mph—the first named storm to make landfall in Florida since Andrea in 2013.

Earlier this year, Hurricane Alex became only the second hurricane on record to form in the month of January, sweeping through The Azores as a tropical storm.

Prepare for the Worst

The insurance sector has been substantially re-shaped since the last large catastrophe loss—by M&A, the influx of new capital—meaning new people, new relationships, even new claims procedures and personnel

It’s an entirely new landscape, entirely untested—how will it respond when a catastrophe hits and claims and losses mount?

From first responders to catastrophe modelers, one piece of advice never changes—be prepared.

That means understanding your exposures and accumulations and owning your own view of risk.

You can’t control or avoid catastrophes, but you can manage and mitigate their effects. Being prepared is the first step.