Tag Archives: (re)insurance

EXPOSURE Magazine Snapshots: Evolution of the Insurer DNA

This is a taster of an article published in the second edition of EXPOSURE magazine.  Click here and download your full copy now.

6 Apr 2017 - Evolution of Insurer DNA blog image banner 720 x 168Many in (re)insurance recognize that the industry is at a tipping point. Rapid technological change, disruption through new, more efficient forms of capital, and an evolving risk landscape are challenging industry incumbents like never before. EXPOSURE magazine reported that inevitably the winners will be those who find ways to harmonize analytics, technology, industry innovation, and modeling.

“Disruptive innovation” is increasingly obvious in areas such as personal lines insurance, with disintermediation, the rise of aggregator websites and the Internet of Things (IoT).  In the commercial insurance and reinsurance space, disruptive technological change has been less obvious, but behind the scenes the industry is undergoing some fundamental changes.

The tipping point, the “Uber” moment has yet to arrive in reinsurance, according to Michael Steel, global head of solutions at RMS. “­The change we’re seeing in the industry is constant. We’re seeing disruption throughout the entire insurance journey. It’s not the case that the industry is suffering from a short-term correction and then the market will go back to the way it has done business previously. ­ The industry is under huge competitive pressures and the change we’re seeing is permanent and it will be continuous over time.”

While it is impossible to predict exactly how the industry will evolve going forward, it is evident that tomorrow’s leading (re)insurance companies will share certain attributes. ­ This includes a strong appetite to harness data and invest in new technology and analytics capabilities, the drive to differentiate and design new products and services, and the ability to collaborate. According to Eric Yau, general manager of software at RMS, the goal of an analytic-driven organization is to leverage the right technologies to bring data, workflow and business analytics together to continuously drive more informed, timely and collaborative decision making across the enterprise.

“New technologies play a key role and while there are many choices with the rise of insurtech firms, history shows us that success is achieved only when the proper due diligence is done to really understand and assess how these technologies enable the longer-term business strategy, goals and objectives.” says Yau. Yau also believes that one of the most important ingredients to success is the ability to effectively blend the right team of technologists, data scientists and domain experts who can work together to understand and deliver upon these key objectives.

Looking for Success in this New World

Which factors will help companies stand out and compete in the future?  EXPOSURE asked industry experts for their views on the attributes that winning companies will share:

The Race for Millennial Talent:  The most successful companies will look to attract and retain the best talent, says Rupert Swallow, co-founder and CEO of Capsicum Re, with succession planning that puts a strong emphasis on bringing Millennials up through the ranks. “­There is a huge difference between the way Millennials look at the workplace and live their lives, versus industry professionals born in the 1960s or 1970s — the two generations are completely different,” says Swallow. “­ Those guys [Millennials] would no sooner write a check to pay for something than fly to the moon.”

Collaboration is the Key: There are numerous examples of tie-ups between (re)insurance industry incumbents and tech firms, to leverage technology – or insurtech – expertise, to get closer to the original risk. ­ One example of a strategic collaboration is MGA Attune, set up last year by AIG, Hamilton Insurance Group, and affiliates of Two Sigma Investments. ­ Through the partnership, AIG gained access to Two Sigma’s vast technology and data-science capabilities to grow its market share in the U.S. small to mid-sized commercial insurance space.

Blockchain:  Blockchain offers huge potential to reduce some of the significant administrative burdens in the industry, thinks Kurt Karl, chief economist at Swiss Re. “Blockchain for the reinsurance space is an efficiency tool. And if we all get more efficient, you are able to increase insurability because your prices come down, and you can have more affordable reinsurance and therefore more affordable insurance. So I think we all win if it’s a cost saving for the industry.”

“­The challenge for the industry is to remain relevant to our customers,” says RMS’ Michael Steel. “­Those that fail to adapt will get left behind. To succeed you’re going to need greater information about the underlying risk, the ability to package the risk in a different way, to select the appropriate risks, differentiate more, and construct better portfolios.”

For the full article and more insight for the insurance industry, click here and download your full copy of EXPOSURE magazine now.

Watch Video: Eric Yau – Managing Risk is an Interconnected Process

Eric Yau, general manager, software business unit at RMS, said those managing risk should keep in mind that risk selection is part of an overall process that affects capacity and portfolio strategy. Yau spoke with A.M. BestTV at the Exceedance 2017 conference.

For more information on RMS(one)®, a big data and analytics platform built from the ground-up for the insurance industry, and solutions such as Risk Modeler and Exposure Manager, please click here.

EXPOSURE Magazine Snapshots: The Analytics Driven Organization

This is a taster of an article published in the second edition of EXPOSURE magazine.  Click here and download your full copy now.5 Apr 2017 - Exposure Analytics Org image with Exposure masthead

Farhana Alarakhiya, vice president, products at RMS, writes… In my recent article in EXPOSURE magazine, I was interested in exploring how firms in the insurance sector can move towards building a more analytics-driven organization.  Being analytics-driven translates to being an agile business, and in a turbulent market landscape, building underwriting agility is becoming critical to business survival.

There is no doubt we have seen revolutionary technological advances and an explosion of new digital data sources, which has reinvented the core disciplines of insurers over the past 15 years.  Many (re)insurers also see big data and analytics (BD&A) as a “silver bullet” to provide competitive advantage and address their current market challenges.

Similar to other industries who continue to invest heavily in BD&A to secure their position and open a new chapter of growth, the insurance sector is also ramping up investment, in open BD&A platforms such as RMS(one)®, which is purpose-built for the insurance industry.  But although there is a real buzz around BD&A, what may be lacking is a big data strategy specifically for evolving pricing, underwriting and risk selection, areas which provide huge potential gains for firms.

With the opportunity for our industry to gain transformational agility in analytics now within reach, we need to be conscious of how to avoid DRIP, being data rich, but information poor, with too much focus being on data capture, management, and structures, at the expense of creating useable insights that can be fed to the people at the point of impact.  Regulation is not the barrier to success either, many other regulated business areas have transformed their business and gained agility through effective analytics.

Please read the full article in EXPOSURE magazine to discover more about the three main lessons insurers can learn from other businesses who have their BD&A recipe just right, but here’s a short summary:

Lesson #1 – Delivering Analytics to the Point of Impact

Being reliant on back office processes for analytics is common for insurers, but doesn’t work for a frontline healthcare worker, for example.  Data analysts are rare in this sector, because a healthcare worker has analytics designed around their role, to support their delivery.  If you look at a portfolio manager in the insurance sector, they typically work in tandem with an analyst to get relevant data, let alone insight, which compromises their ability to perform effectively.

Lesson #2 – Ensuring Usability

Recognizing the workflow of an analytics user and giving due consideration to the veracity of the data provided to reduce uncertainty is vital. Looking at our healthcare example, analytics tools used by doctors to diagnose a patient’s condition use standardized information – age, sex, weight, height, ethnicity, address – and the patient’s symptoms.

They are provided not with a defined prognosis but a set of potential diagnoses accompanied by a probability score and the sources. Imagine this level of analytical capability provided in real-time at the point of underwriting, where the underwriter not only has access to the right set of analytics, they also have a clear understanding of other options and underlying assumptions.

Lesson #3 – Integration into the Common Workflow

To achieve data nirvana, BD&A output needs to integrate naturally into daily business-as-usual operations. When analytics are embedded directly into the daily workflow, there is a far higher success rate of it being put to effective use.  With customer service technology, all the required systems are directly integrated into the customer agents’ software for a holistic view of the customer.  Using platforms built and designed with open architecture allows legacy systems or your specific intellectual property-intensive processes to be integrated, for access to analytics that allow them to derive insights as part of the daily workflow for every risk they write.

This is a taster of an article published in the second edition of EXPOSURE magazine.  Click here and download your full copy now.

Watch Video: Farhana Alarakhiya – The Data Challenge Is Getting It to the Right People

Farhana Alarakhiya, vice president, products at RMS, said insurers are responding to the allure of big data, but must focus on turning voluminous data into meaningful insights. Alarakhiya spoke with A.M. BestTV at the Exceedance 2017 conference.

For more information of RMS(one)®, a big data and analytics platform built from the ground-up for the insurance industry, and solutions such as Risk Modeler and Exposure Manager, please click here.

The Age of a Roof and The Price You Pay: New Analysis of Hurricane Risk in the U.S.

RMS has completed research on hurricane risk to single-family dwellings using an improved understanding of roof age, which can lead to more accurate loss projections using our models

Residential gable end roof failure in the Bahamas, observed following Hurricane Matthew

Residential gable end roof failure in the Bahamas, observed following Hurricane Matthew

Weak roofs mean losses during hurricanes. During reconnaissance trips to the southeast U.S. and the Bahamas following Hurricane Matthew last fall, RMS experts saw ample evidence of this simple fact.  Their on-the-ground survey highlighted everything from shingle and tile damage to complete roof failures.

Roof weakness significantly influences RMS’ view of structural vulnerability in our North Atlantic Hurricane models, which can factor in a roof’s age, covering, and shape into calculations of potential loss. However, this valuable property data is not captured by many insurers, and this could represent a missed business opportunity to improve underwriting – whether it be pricing or risk selection.

Extending the Data, Refining the Insights

RMS already has a dataset of hurricane claims from over one million single-family dwelling (SFD) homes in Florida and the northeast U.S., representing $240 billion in total insured value. However, this dataset lacks roof characteristics for a majority of the homes, so we augmented it with roof age information obtained from BuildFax, which holds detailed building characteristics for over 90 million properties in over 10,000 U.S. cities and counties. From this enhanced dataset we found:

  • About 70 percent of Florida homes (SFDs) had roofs aged 10 years or older at the time of the 2004-05 hurricanes
  • Roughly half of the Northeast homes (SFDs) had roofs aged 20 years or older at the time of Superstorm Sandy (2012)
  • Only 20% of all homes (SFDs) still had their original roofs, although this proportion was lower for coastal properties than for inland properties

So what was the relationship between roof age and losses? In the second stage of our research, our vulnerability modelers paired the exposure data with 182,000 hurricane claims, totaling $2.25 billion in paid losses, to look for patterns related to roof age.

graph claim severity 1

Normalized severity of Florida claims from the 2004-05 hurricanes, by roof age and selected wind speed bands, for all risk classes

Normalized severity of Northeast claims from Sandy, by roof age and selected wind speed bands, for all risk classes.

Normalized severity of Northeast claims from Sandy, by roof age and selected wind speed bands, for all risk classes

 

 

 

 

 

 

 

 

 

As expected, we found that homes with older roofs generally corresponded with more claims, and claims of greater severity. This was most evident at the low wind speeds experienced in the Northeast U.S. during Superstorm Sandy, as well as at higher wind speeds experienced in the Florida hurricanes. These graphs show that buildings in Florida with a roof older than 20 years are associated with claims that are between 50-100% more severe, compared with buildings having a roof less than five years old. A similar trend appears in the Northeast, but is muted because of the smaller dataset.

That’s the picture from historical data. But what about modeling potential future events? To answer that question we analyzed the enlarged Florida dataset, focusing on how roof age at a particular location compares to the industry average for that region.

patchwork map

Change in modeled AAL by Florida county when including roof age information from BuildFax

The change in modeled average annual loss (AAL) by county shows a patchwork of increased and decreased risk that corresponds to the average roof age of properties in each county.

So we can see that using roof age data leads to significant differences in modeled loss within regions.

That’s a valuable insight in itself. But we decided to drill down a little deeper.

 

 

 

From counties to ZIP codes to individual locations

Although the maximum change in AAL was less than 10% at the county level, changes of up to 20% were observed at the level of ZIP codes. These results show that improved understanding of predominant roof age could influence a company to change its regional underwriting strategy or refine its rating territories.

Going more granular still, within each county and ZIP code there is variation in the roof age of individual homes and this is critical to consider when writing new business. The scatter plot below shows the change in AAL at individual locations. Those homes with older roofs produce higher than average AAL and vice versa.

red blob map

Change in modeled AAL by location when including roof age information. “Location AAL” (x-axis) represents AAL without roof age

So when we go down to the level of individual locations the impact of roof age data leads to loss changes of up to 50%, demonstrating higher significance than at the regional level. For high hurricane risk locations in Florida with large baseline AALs, this change translates into substantial dollar amounts. That’s crucial to know, revealing key opportunities to improve underwriting practices. For instance, companies might choose to quote more competitively on price for properties with newer roofs.

Unsurprisingly, over time strengthened building codes and practices have led to stronger roofs that are more resilient to hurricane damage. But this research tells us much more – the sheer magnitude of modeled loss changes observed was significant, with clear implications for profitability, as explained by BuildFax CEO Holly Tachovsky:

“These results reveal key opportunities to improve underwriting practices, including pricing and risk selection. A focus on roof age can be the difference-maker for loss ratios in certain geographies. As a result, we see a growing level of sophistication among carriers that want to rate and select with a higher degree of accuracy.”

RMS remains committed to partnerships with industry experts like BuildFax to communicate the business benefits of emerging trends in the (re)insurance space.

Indonesia’s Protection Gap – How the Sumatra Earthquake Shows that Coverage Must Spread

On December 7, 2016, a shallow magnitude 6.5 earthquake struck northern Sumatra in Indonesia, severely damaging or destroying more than ten thousand homes and many businesses, as well as causing over a hundred deaths. The disaster struck a poorer area away from the major cities, where the standards of building design, construction methods, and material quality are not sufficient to withstand such an earthquake.

sumatra1

USGS Shake map for Mw 6.5 Earthquake

We have up-to-date research on local building design and construction practices in Indonesia, which we have incorporated into the latest version of the RMS® Indonesia Earthquake Model. This research was done last year when members of the RMS vulnerability team, including me, visited southeast Asia as part of the process to update the model. We held workshops with local earthquake engineering experts who practice there, and attended an earthquake engineering conference, as well as visiting commercial and industrial buildings, including those under construction, to see first-hand how they were designed and built.

sumatra2

A workshop with local experts

sumatra3

International Conference – Jogja Earthquake in Reflection (May, 2016)

This on-the-ground research provided insights into Indonesia’s rules and practices around construction, seismic design, code enforcement, as well as information on the relative quantities of different types of buildings in the country. We discovered significant differences between mainstream construction and those buildings covered by earthquake insurance, namely:

  • Past earthquakes have demonstrated that single family dwellings and/or low rise buildings are the most vulnerable building types compared to those built for commercial and industrial use, because of a lack of engineering design, poor construction, and lower material quality.
  • Buildings outside of major cities are mostly low rises and they may not be designed for earthquake risk.
  • Major cities such as Jakarta, Bandung, and Surabaya enforce a strict structural design review process for the construction of mid- and high-rise buildings.
  • Insurance penetration rates are higher for commercial and industrial buildings in and near major cities, with much lower penetration for residential properties in rural areas.

It’s perhaps not surprising that if poorer communities have less insurance protection, that they also cannot afford to invest in the higher quality construction that is designed to better withstand earthquakes. This is one of the primary reasons for the ‘protection gap’. As these countries become more developed, there’s the potential for that gap to start closing. In fact, Indonesia is one of the fastest growing economies in southeast Asia, with the property insurance and (re)insurance market expanding rapidly.

But as the earthquake disaster demonstrated, there are still many poorer regions with low insurance penetration which are also prone to repeated natural disasters. Sadly, there is still a long way to go before people in those places benefit from the resilience in their built environment which other, richer parts of the world may take for granted.

Understanding Risk Accumulations in Taiwan’s Science Parks

“The 6.4 magnitude Tainan earthquake in February 2016 resulted in a sizeable insured loss from the high-tech industrial risks and reminded the insurance industry of the potential threat from the risk accumulated in science parks.” (A.M. Best Special Report, Sept 2016)

Reading the sentence above you might be forgiven for wondering why science parks would give insurers and reinsurers any particular cause for concern. But consider this statistic: although Taiwan’s three major science and industrial parks occupy only 0.1% of the island’s total land mass, they represent 16% of Taiwan’s overall manufacturing – they are hugely significant, both economically and with regards to the insured exposure in Taiwan.

For example, the Hsinchu Science Park (HSP), known for semiconductor production, employs more than 150,000 people and contributes over $32 billion in revenues – approximately 6% of national GDP. By one estimate HSP represents over $319 billion in total insured values. In addition, some of the latest high tech areas within HSP, such as advanced “clean rooms,” present additional challenges due to their vulnerability to ground shaking or power interruption. The importance of this risk was observed in February’s Tainan earthquake where some significant losses to high-tech industrial risks were caused by damage to the equipment and the related business interruption due to power outage.

Improving data quality for advanced and detailed modeling is an important way to manage these risks, concludes the A.M. Best report quoted above. This is so as to accurately assess the potential loss impact on insurers’ books. RMS has already been analysing earthquake risk in Taiwan for 12 years – long before this year’s Mw 6.4 event – and in that time our view of seismic risk in Taiwan has not changed, since our model benefits from spectral response-based hazard and damage functions, that even include local liquefaction and landslide susceptibilities.

The 1999 Chi-Chi Earthquake (known in Taiwan as the 921 Earthquake) was the key event in building the RMS® Taiwan Earthquake Model in terms of the quake’s seismicity, ground motion, soil secondary effects and building response. Since then there have been no significant events to justify a re-calibration of the components of the model. In fact, the damages observed in this year’s event were broadly in line with RMS’ expectations and validated the robustness of the current model.

But although A.M. Best views the Taiwan insurance industry as prudently managed with relatively high catastrophe management capability, there are still lessons to be learnt from the 2016 event, and RMS has solutions which offer additional insight into understanding the risk posed by these business parks in Taiwan.

Concentration of Exposure into Science Parks

The RMS® Asia Industrial Clusters Catalogs were released in 2014 to identify hotspots of exposure, and profile their risk. The locations and geographic extent of the science parks within Taiwan are detailed to help understand risk accumulations for industrial lines and develop more robust risk management strategies.

taiwanblog1

Example of industrial cluster captured in the RMS Taiwan Industrial Clusters Catalog. The red outline illustrates the digitized boundaries of the Formosa Petrochemical Co. Plant in Yunlin Hsien.

High Fragility of the Semiconductor Industry

For coding of Industrial Plants, the RMS® Industrial Facilities Model (IFM) captures the unique nature of different industrial risks, as a high percentage of property value is often associated with machinery and equipment (M&E) and stock. This advanced vulnerability model supports the earthquake model to define the damageability of a comprehensive set of industrial facilities more accurately, and calculate the financial risk to these specific types of facilities, including building, contents, and business interruption (BI) loss estimates. The IFM differentiates the risks for different types of business within the science parks, and highlights the higher fragility of semiconductor plants compared to other industrial units, as shown below.

taiwanblog2

Lessons Learnt?

The huge damage from the 1999 Chi Chi earthquake has not halted the rapid development of Taiwan’s science parks in this seismically active area – indeed the island’s third biggest science park has since been built there. But this year’s comparatively small Mw 6.4 event further highlighted the substantial exposures concentrated within this sector, reminding the industry of the potential for significant losses without sound accumulation management practices, informed by the best modeling insights.

See How Quickly and Easily You Can Access the Exposure Metrics That Matter

Exposure Manager is a risk management solution that provides executives, underwriters, risk analysts, and other decision-makers with the exposure analytics needed to offer a comprehensive view of risk and understand loss potential.

As the first solution released on the RMS(one) platform, Exposure Manager was developed based on the understanding that organizations not only need quick and reliable assessments of exposure concentrations, but also the right tools to ensure they can access key metrics and insights.

The videos below illustrate two of the important capabilities that enhance users’ ability to build portfolio intuition faster and quickly access the metrics that are most important.

Build Portfolio Intuition Faster provides insights into how Exposure Manager enables customers to quickly and efficiently derive deeper portfolio insights using an intuitive and user-friendly interface.

With a customizable interface that conveys the information that’s most important to the user, Exposure Manager’s analytics, enabled by an intuitive best-in-class user experience, can be configured without knowledge of SQL or support from IT.

This enhances the ability for customers to create quick insights into their portfolio or perform a deep dive into their book to make quick assessments.

Access Metrics That Matter shows how Exposure Manager leverages the RMS financial model to provide an exposed limit metric. This offers a consistent view of loss potential to enable precise identification of loss drivers.

The flexible interface provides users with precise control to quickly make informed decisions about their book and help identify threats and opportunities in the portfolio.

All of these benefits allow customers to become more incisive about their portfolio.

Earthquake Hazard: What Has New Zealand’s Kaikoura Earthquake Taught Us So Far?

The northeastern end of the South Island is a tectonically complex region with the plate motion primarily accommodated through a series of crustal faults. On November 14, as the Kaikoura earthquake shaking began, multiple faults ruptured at the same time culminating in a Mw 7.8 event (as reported by GNS Science).

The last two weeks have been busy for earthquake modelers. The paradox of our trade is that while we exist to help avoid the damage this natural phenomenon causes, the only way we can fully understand this hazard is to see it in action so that we can refine our understanding and check that our science provides the best view of risk. Since November 14 we have been looking at what Kaikoura tells us about our latest, high-definition New Zealand Earthquake model, which was designed to handle such complex events.

Multiple-Segment Ruptures

With the Kaikoura earthquake’s epicenter at the southern end of the faults identified, the rupture process moved from south to north along this series of interlinked faults (see graphic below). Multi-fault rupture is not unique to this event as the same process occurred during the 2010 Mw 7.2 Darfield Earthquake. Such ruptures are important to consider in risk modeling as they produce events of larger magnitude, and therefore affect a larger area, than individual faults would on their own.

Map showing the faults identified by GNS Sciences as experiencing surface fault rupture in the Kaikoura Earthquake.
Source: http://info.geonet.org.nz/display/quake/2016/11/16/Ruptured +land%3A+observations+from+the+air

In keeping with the latest scientific thinking, the New Zealand Earthquake HD Model provides an expanded suite of events that represent complex ruptures along multiple faults. For now, these are included only for areas of high slip fault segments in regions with exposure concentrations, but their addition increases the robustness of the tail of the Exceedance Probability curve, meaning clients get a better view of the risk of the most damaging, but lower probability events.

Landsliding and Liquefaction

While most property damage has been caused directly by shaking, infrastructure has been heavily impacted by landsliding and, to a lesser extent, liquefaction. Landslides and slumps have occurred across the region, most notably over Highway 1, an arterial route. The infrastructure impacts of the Kaikoura earthquake are a likely dress rehearsal for the expected event on the Alpine Fault. This major fault runs 600 km along the western coast of the South Island and is expected to produce an Mw 8+ event with a probability of 30% in the next 50 years, according to GNS Science.

As many as 80 – 100,000 landslides have been reported in the upper South Island, with some creating temporary dams over rivers and in some cases temporary lakes (see below). These dams can fail catastrophically, sending a sudden increase of water flow down the river.

 

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Examples of rivers blocked by landslides photographed by GNS Science researchers.

Source: http://info.geonet.org.nz/display/quake/2016/11/18/ Landslides+and+Landslide+dams+caused +by+the+Kaikoura+Earthquake

 

 

 

 

 

 

 

 

Liquefaction occurred in discrete areas across the region impacted by the Kaikoura earthquake. The Port of Wellington experienced both lateral and vertical deformation likely due to liquefaction processes in reclaimed land. There have been reports of liquefaction near the upper South Island towns (Blenheim, Seddon, Ward), but liquefaction will not be a driver of loss in the Kaikoura event to the extent it was in the Christchurch earthquake sequence.

RMS’ New Zealand Earthquake HD Model includes a new liquefaction component that was derived using the immense amount of new borehole data collected after the Canterbury Earthquake Sequence in 2010-2011. This new methodology considers additional parameters, such as depth to the groundwater table and soil-strength characteristics, that lead to better estimates of lateral and vertical displacement. The HD model is the first probabilistic model with a landslide susceptibility component for New Zealand.

Tsunami

The Kaikoura Earthquake generated tsunami waves that were observed in Kaikoura at 2.5m, Christchurch at 1m, and Wellington at 0.5m. The tsunami waves arrived in Kaikoura significantly earlier than in Christchurch and Wellington indicating that the tsunami was generated near Kaikoura. The waves were likely generated by offshore faulting, but also may be associated with submarine landsliding. Fortunately, the scale of the tsunami waves did not produce significant damage. RMS’ latest New Zealand Earthquake HD Model captures tsunami risk due to local ocean bottom deformation caused by fault rupture, and is the first model in the New Zealand market to do this, that is built from a fully hydrodynamic model.

Next Generation Earthquake Modeling at RMS

Thankfully the Kaikoura earthquake seems to have produced damage that is lower than we might have seen had it hit a more heavily populated area of New Zealand with greater exposures – for detail on damage please see my other blog on this event.

But what Kaikoura has told us is that our latest HD model offers an advanced view of risk. Released only in September 2016, it was designed to handle such a complex event as the Kaikoura earthquake, featuring multiple-segment ruptures, a new liquefaction model at very high resolution, and the first landslide susceptibility model for New Zealand.

New Zealand’s Kaikoura Earthquake: What Have We Learned So Far About Damage?

The Kaikoura Earthquake of November 14 occurred in a relatively low population region of New Zealand, situated between Christchurch and Wellington. The largest town close to the epicentral region is Blenheim, with a population near 30,000.

Early damage reports indicate there has been structural damage in the northern part of the South Island as well as to numerous buildings in Wellington. While most of this has been caused directly by shaking, infrastructure and ports across the affected region have been heavily impacted by landsliding and, to a lesser extent, liquefaction. Landslides and slumps have occurred across the northeastern area of the South Island, most notably over Highway 1, severing land routes to Kaikoura – a popular tourist destination.

The picture of damage is still unfolding as access to badly affected areas improves. At RMS we have been comparing what we have learned from this earthquake to the view of risk provided by our new, high-definition New Zealand Earthquake model, which is designed to improve damage assessment and loss quantification at location-level resolution.

No Damage to Full Damage

The earthquake shook a relatively low population area of the South Island and, while it was felt keenly in Christchurch, there have been no reports of significant damage in the city. The earthquake ruptured approximately 150 km along the coast, propagating north towards Wellington. The capital experienced ground shaking intensities at the threshold for damage, producing façade and internal, non-structural damage in the central business district. Although the shaking intensities were close to those experienced during the Cook Strait sequence in 2013, which mostly affected short and mid-rise structures, the longer duration and frequency content of the larger magnitude Kaikoura event has caused more damage to taller structures which have longer natural periods.

From: Wellington City Council

Within Wellington, cordons are currently in place around a few buildings in the CBD (see above) as engineers carry out more detailed inspections. Some are being demolished or are set to be, including a nine-story structure on Molesworth Street and three city council buildings. It should be noted that most of the damage has been to buildings on reclaimed land close to the harbor where ground motions were likely amplified by the underlying sediments.

From: http://www.stuff.co.nz/national/86505695/quakehit-wellington-building-at-risk-of-collapse-holds-up-overnight; The building on Molesworth street before the earthquake (L) and on Tuesday (R).

From: http://www.stuff.co.nz/national/86505695/quakehit-wellington-building-at-risk-of-collapse-holds-up-overnight; The building on Molesworth street before the earthquake (L) and after on November 16 (R).

Isolated incidences of total damage in an area of otherwise minor damage demonstrate why RMS is moving to the new HD financial modeling framework. The RMS RiskLink approach applies a low mean damage ratio across the area, whereas RMS HD damage functions allow for zero or total loss – as well as a distribution in between which is sampled for each event for each location. The HD financial modeling framework is able to capture a more realistic pattern of gross losses.

Business Interruption

The Kaikoura Earthquake will produce business interruption losses from a variety of causes such as direct property or content damages, relocation costs, or loss of access to essential services (i.e. power and water utilities, information technology) that cripple operations in otherwise structurally sound buildings. How quickly businesses are able to recover depends on how quickly these utilities are restored. Extensive landslide damage to roads means access to Kaikoura itself will be restricted for months. The New Zealand government has announced financial assistance packages for small business to help them through the critical period immediately after the earthquake. Similar assistance was provided to businesses in Christchurch after the Canterbury Earthquake Sequence in 2010-2011.

That earthquake sequence and others around the world have provided valuable insights on business interruption, allowing our New Zealand Earthquake HD model to better capture these impacts. For example, during the Canterbury events, lifelines were found to be repaired much more quickly in urban areas than in rural areas, and areas susceptible to liquefaction were associated with longer down times due to greater damage to underground services. The new business interruption model provides a more accurate assessment of these risks by accounting for the influence of both property and contents damage as well as lifeline downtime.

It remains to be seen how significant any supply chain or contingent business interruption losses will be. Landslide damage to the main road and rail route from Christchurch to the inter-island ferry terminal at Picton has disrupted supply routes across the South Island. Alternative, longer routes with less capacity are available.

Next Generation Earthquake Modeling at RMS

RMS designed the update to its New Zealand Earthquake High Definition (HD) model, released in September 2016, to enhance location-level damage assessment and improve the gross loss quantification with a more realistic HD financial methodology. The model update was validated with billions of dollars of claims data from the 2010-11 Canterbury Earthquake Sequence.

Scientific and industry lessons learned following damaging earthquakes such as last month’s in Kaikoura and the earlier event in Christchurch increase the sophistication and realism of our understanding of earthquake risk, allowing communities and businesses to shift and adapt – so becoming more resilient to future catastrophic events.

Prudential Regulation Authority on the Challenges Facing Cyber Insurers

Most firms lack clear strategies and appetites for managing cyber risk, with a shortage of cyber domain knowledge noted as a key area of concern. So said the Prudential Regulation Authority, the arm of the Bank of England which oversees the insurance industry, in a letter to CEOs last week.

This letter followed a lengthy consultation with a range of stakeholders, including RMS, and identified several key areas where insurance firms could and should improve their cyber risk management practices. It focussed on the two distinct types of cyber risk: affirmative and silent.

Affirmative cover is explicit cyber coverage, either offered as a stand-alone policy or as an endorsement to more traditional lines of business. Silent risk is where cover is provided “inadvertently” through a policy that was typically never designed for it. But this isn’t the only source of silent risk: it can also leak into policies where existing exclusions are not completely exhaustive. A good example being policies with NMA 2914 applied, which excludes cyber losses except for cases where physical damage is caused in any cyber-attack (eg. by fire or explosion).

The proliferation of this silent risk across the market is highlighted as one of the key areas of concern by the PRA. It believes this risk is not only material, but it is likely to increase over time and has the potential to cause losses across a wide range of classes, a sentiment we at RMS would certainly echo.

The PRA intervention shines a welcome spotlight and adds to the growing pressure on firms to do more to improve their cyber risk management practices. These challenges facing the market have been an issue for some time, but the how do we help the industry address them?

The PRA suggests firms with cyber exposure should have a clearly defined strategy and risk appetite owned by the board and risk management practices that include quantitative and qualitative elements.

At RMS our cyber modeling has focussed on providing precisely this insight, helping many of the largest cyber writers to quantify both their silent and affirmative cyber risk, thus allowing them to focus on growing cyber premiums.

If you would like to know more about the RMS Cyber Accumulation Management System (released February 2016), please contact cyberrisk@rms.com.

Shrugging Off a Hurricane: A Three Hundred Year Old Culture of Disaster Resilience

If a global prize was to be awarded to the city or country that achieves the peak of disaster resilience, Bermuda might be a fitting first winner.

This October’s Hurricane Nicole made direct landfall on the island. The eyewall tracked over Bermuda with maximum measured windspeeds close to 120 mph. Nonetheless there were there were no casualties. The damage tally was principally to fallen trees, roadway debris, some smashed boats and many downed utility poles. The airport opened in 24 hours, with the island’s ferries operating the following day.

Bermuda’s performance through Nicole was exemplary. What’s behind that?

Since its foundation in 1609 when 150 colonists and crew were shipwrecked on the island, Bermuda has got used to its situation at the heart of hurricane alley. Comprising 21 square miles of reef and lithified dunes, sitting out in the Atlantic 650 miles west of Cape Hatteras, a hurricane hits the island on average once every six or seven years. Mostly these are glancing blows, but once or twice a century Bermuda sustains direct hits at Category 3 or 4 intensity. Hurricane Fabian in 2003 was the worst of the recent storms, causing $300 million of damage (estimated to be worth $650 million, accounting for today’s higher prices and greater property exposure). The cost of the damage from Hurricane Gonzalo in 2014 was about half this amount.

How did Bermuda’s indigenous building style come to adopt such a high standard of wind resistance? It seems to go back to a run of four hurricanes at the beginning of the 18th Century. First, in September 1712 a hurricane persisted for eight hours destroying the majority of wooden buildings. Then twice in 1713 and again more strongly in 1715 the hurricane winds ruined the newly rebuilt churches. One hurricane can seem like an exception, four becomes a trend. In response, houses were constructed with walls of massive reef limestone blocks, covered by roofs tiled with thick slabs of coral stone: traditional house styles that have been sustained ever since.

The frequency of hurricanes has helped stress test the building stock, and ensure the traditional construction styles have been sustained. More recently there has been a robust and well-policed building code to ensure adequate wind resistance for all new construction on the island.

Yet resilience is more than strong buildings. It also requires hardened infrastructure, and that is where Bermuda has some room for improvement. Still dependent on overhead power lines, 90 percent of the island’s 27,000 houses lost power in Hurricane Nicole – although half of these had been reconnected by the following morning and the remainder through that day. Mobile phone and cable networks were also back in operation over a similar timescale. Experience of recent hurricanes has ensured an adequate stockpile of cable and poles.

Expert Eyes on the Island

It helps that there is an international reinsurance industry on the island, with many specialists in the science of hurricanes and the physics and engineering of building performance on hand to scrutinize the application of improved resilience. Almost every building is insured, giving underwriters oversight of building standards. Most importantly, the very functioning of global reinsurance depends on uninterrupted connection with the rest of the world, as well as ensuring that on-island staff are not distracted by having to attend to their family’s welfare.

Bermuda’s experience during Nicole would merit the platinum standard of resilience adopted by the best businesses: that all functions can be restored within 72 hours of a disaster. The Bermuda Business Development Agency and the Association of Bermuda Insurers and Reinsurers were fulsome in their praise for how the island had withstood the hurricane. The strong and widely-owned culture of preparedness, reflects the experience of recent storms like Gonzalo and Fabian.

Stephen Weinstein, general counsel at RenaissanceRe, commented “It’s remarkable that one day after a major hurricane strike, Bermuda is open for business, helping finance disaster risk worldwide, and poised to welcome back business visitors and vacationers alike.”

In early 2017, RMS will issue an update to Bermuda wind vulnerability in the version 17 software release as part of a broader update to the 33 islands and territories covered by the North Atlantic Hurricane Models. Updates to Bermuda vulnerability will consider past hurricane observations and the latest building code research.