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Helen Yates
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September 06, 2019
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 2019 biennial insurance stress test, the U.K. insurance industry regulator  — 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) came up with a series of future climate change scenarios, which it asked (re)insurers to use as a basis for stress-testing the impact on their assets and liabilities. The PRA stress test came 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). The submission deadline for the stress-tested scenarios ended on 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 was pressure on clients to respond to this because those that don’t participate will probably come under greater scrutiny” Callum Higgins RMS RMS was 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 examined 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 included 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 included 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 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  provided them with industrywide factors, which allowed for the approximation of losses under the PRA assumptions but will likely not accurately reflect the impact on their portfolios. For this reason, we could also run (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.

NIGEL ALLEN
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September 06, 2019
What a Difference

As the insurance industry’s Dive In Festival continues to gather momentum, EXPOSURE examines the factors influencing the speed at which the diversity and inclusion dial is moving September 2019 marks the fifth Dive In Festival, a global movement in the insurance sector to support the development of inclusive workplace cultures. An industry phenomenon, it has ballooned in size from a London-only initiative in 2015 attracting 1,700 people to an international spectacle spanning 27 countries and reaching over 9,000 people in 2018. That the event should gather such momentum clearly demonstrates a market that is moving forward. There is now an industrywide acknowledgement of the need to better reflect the diversity of the customer base within the industry’s professional ranks. The Starting Point As Pauline Miller, head of talent development and inclusion (D&I) at Lloyd’s, explains, the insurance industry is a market that has, in the past, been slow to change its practitioner profile. “If you look at Lloyd’s, for example, for nearly three hundred years it was a men-only environment, with women only admitted as members in December 1969. “It’s about bringing together the most creative group of people that represent different ways of thinking that have evolved out of the multiple factors that make them different” Pauline Miller Lloyd’s “You also have to recognize that the insurance industry is not as far along the diversity and inclusion journey compared to other sectors,” she continues. “I previously worked in the banking industry, and diversity and inclusion had been an agenda issue in the organization for a number of years. So, we must acknowledge that this is a journey that will require multiple more steps before we really begin breaking down barriers.” However, she is confident the insurance industry can quickly make up ground. “By its very nature, the insurance market lends itself to the spread of the D&I initiative,” Miller believes. “We are a relationship-based business that thrives on direct contact, and our day-to-day activities are based upon collaboration. We must leverage this to help speed up the creation of a more diverse and inclusive environment.” The positive effects of collaboration are already evident in how this is evolving. Initiatives like Dive In, a weeklong focus on diversity and inclusion, within other financial sectors have tended to be confined to individual organizations, with few generating the level of industrywide engagement witnessed within the insurance sector. However, as Danny Fisher, global HR business partner and EMEA HR manager at RMS, points out, for the drive to gain real traction there must be marketwide consensus on the direction it is moving in. “There is always a risk,” he says, “that any complex initiative that begins with such positive intent can become derailed if there is not an understanding of a common vision from the start, and the benefits it will deliver. “There also needs to be better understanding and acknowledgement of the multitude of factors that may have contributed to the uniformity we see across the insurance sector. We have to establish why this has happened and address the flaws in our industry contributing to it.” It can be argued that the insurance industry is still composed of a relatively homogeneous group of people. In terms of gender disparity, ethnic diversity, and people of different sexual orientations, from different cultural or social backgrounds, or with physical or mental impairments, the industry recognizes a need to improve.  Diversity is the range of human differences, including but not limited to race, ethnicity, gender, gender identity, sexual orientation, age, social class, physical ability or attributes, religious or ethical values system, national origin, and political beliefs. “As a market,” Miller agrees, “there is a tendency to hire people similar to the person who is recruiting. Whether that’s someone of the same gender, ethnicity, sexual orientation or from the same university or social background.” “You can end up with a very uniform workforce,” adds Fisher, “where people look the same and have a similar view of the world, which can foster ‘groupthink’ and is prone to bias and questionable conclusions. People approach problems and solutions in the same way, with no one looking at an alternative — an alternative that is often greatly needed. So, a key part of the diversity push is the need to generate greater diversity of thought.” The challenge is also introducing that talent in an inclusive way that promotes the effective development of new solutions to existing and future problems. That broad palette of talent can only be created by attracting and retaining the best and brightest from across the social spectrum within a framework in which that blend of skills, perspectives and opinions can thrive. “Diversity is not simply about the number of women, ethnicities, people with disabilities or people from disadvantaged backgrounds that you hire,” believes Miller. “It’s about bringing together the most creative group of people that represent different ways of thinking that have evolved out of the multiple factors that make them different.” Moving the Dial There is clearly a desire to make this happen and strong evidence that the industry is moving together. Top-level support for D&I initiatives coupled with the rapid growth of industrywide networks representing different demographics are helping firm up the foundations of a more diverse and inclusive marketplace.  But what other developments are needed to move the dial further? “We have to recognize that there is no ‘one-size-fits-all’ to this challenge,” says Miller. “Policies and strategies must be designed to create an environment in which diversity and inclusion can thrive, but fundamentally they must reflect the unique dynamics of your own organization. “We also must ensure we are promoting the benefits of a career in insurance in a more powerful and enticing way and to a broader audience,” she adds. “We operate in a fantastic industry, but we don’t sell it enough. And when we do get that diversity of talent through the door, we have to offer a workplace that sticks, so they don’t simply walk straight back out again.  “For example, someone from a disadvantaged community coming through an intern program may never have worked in an office environment before, and when they look around are they going to see people like themselves that they can relate to? What role models can they connect with? Are we prepared for that?” For Fisher, steps can also be taken to change processes and modernize thinking and habits. “We have to be training managers in interview and evaluation techniques and discipline to keep unconscious bias in check. There has to be consistency with meaningful tests to ensure data-driven hiring decisions. “At RMS, we are fortunate to attract talent from around the world and are able to facilitate bringing them on board to add further variety in solving for complex problems. A successful approach for us, for example, has been accessing talent early, often prior to their professional career.” There is, of course, the risk that the push for greater diversity leads to a quota-based approach.  “Nobody wants this to become a tick-box exercise,” believes Miller, “and equally nobody wants to be hired simply because they represent a particular demographic. But if we are expecting change, we do need measurements in place to show how we are moving the dial forward. That may mean introducing realistic targets within realistic timeframes that are monitored carefully to ensure we are on track. “Ultimately,” she concludes, “what we are all working to do is to create the best environment for the broadest spectrum of people to come into what is a truly amazing marketplace. And when they do, offering a workplace that enables them to thrive and enjoy very successful careers that contribute to the advancement of our industry. That’s what we all have to be working toward.”

NIGEL ALLEN
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September 06, 2019
The Value of Defense

Current flood defenses in the U.K. reduce annual losses from river flooding by £1.1 billion, according to research by RMS Flooding is one of the most significant natural hazards for the U.K. with over five million homes and businesses in England at risk of flooding and coastal erosion, according to the Environment Agency.  Flood barrier in Shropshire, England In 2015, the U.K. government announced a six-year, £2.3 billion investment in flood defenses. But the Environment Agency proposes a further annual investment of £1 billion through 2065 to keep pace with the flood-related impacts of climate change and shifts in exposure levels. Critical to targeted flood mitigation investment is understanding the positive impacts of current defenses. In June 2019, Flood Re* released its Investing in Flood Risk Management and Defenses study, conducted by RMS.  Addressing the financial benefits of existing flood defenses for the first time, data from the RMS® Europe Inland Flood HD Model demonstrated that current infrastructure reduced annual losses from riverine flooding by £1.1 billion. This was based on ground-up losses, using the RMS U.K. Economic Exposure Database covering buildings and contents for residential, commercial, industrial and agricultural, plus business interruption losses.  Critical to targeted flood investment is understanding the positive impacts of current defenses “Our flood model incorporates countrywide defense data sourced from the Environment Agency and the Scottish Flood Defence Asset Database,” says Theresa Lederer, a consultant within the RMS capital and resilience solutions team, “including walls, levees and embankments, carefully reviewed and augmented by RMS experts. Our initial model run was with defenses in place, and then, using the in-built model functionality to enter user-defined defense values, we removed these [defenses in place].”  The differences in average annual loss results between the two analyses was £1.1 billion, with losses increasing from £0.7 billion under current defenses to £1.8 billion in the undefended case. The analysis also revealed a differentiated picture of flood risk and defenses at the regional and local levels. “The savings relative to total inland flood risk are more pronounced in Northern Ireland and England (both over a 50 percent reduction in average annual losses) than Scotland and Wales,” she explains. “But when you view the savings relative to surface-water flood risk only, these are similarly significant across the country, with loss reductions exceeding 75 percent in all regions. This reflects the fact that pluvial flooding, which is kept constant in the analysis, is a bigger loss driver in Scotland and Wales, compared to the rest of the U.K.” Other insights included that the more deprived half of the population — based on the U.K. Townsend Deprivation Index — benefited from 70 percent of the loss reduction. The study also showed that while absolute savings were highest for catastrophic events, the proportion of the savings compared to the overall level of loss caused by such events was less significant. “In the case of 1-in-5-year events,” Lederer says, “river flood defenses prevent approximately 70 percent of inland flood losses.  For 1-in-500-year events this drops to 30 percent; however, the absolute value of those 30 percent is far higher than the absolute savings realized in a 1-in-5-year event. “Should the focus of defenses therefore be on providing protection from major flood events, with potential catastrophic impacts even though return on investment might not be as attractive given their infrequency? Or on attritional losses from more frequent events, which might realize savings more frequently but fail to protect from the most severe events? Finding a balanced, data-driven approach to flood defense investment is crucial to ensure the affordability of sustainable flood resilience.”

Helen Yates
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May 20, 2019
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.”

NIGEL ALLEN
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September 05, 2018
A Model Operation

EXPOSURE explores the rationale, challenges and benefits of adopting an outsourced model function  Business process outsourcing has become a mainstay of the operational structure of many organizations. In recent years, reflecting new technologies and changing market dynamics, the outsourced function has evolved significantly to fit seamlessly within existing infrastructure. On the modeling front, the exponential increase in data coupled with the drive to reduce expense ratios while enhancing performance levels is making the outsourced model proposition an increasingly attractive one. The Business Rationale The rationale for outsourcing modeling activities spans multiple possible origin points, according to Neetika Kapoor Sehdev, senior manager at RMS. “Drivers for adopting an outsourced modeling strategy vary significantly depending on the company itself and their specific ambitions. It may be a new startup that has no internal modeling capabilities, with outsourcing providing access to every component of the model function from day one.” There is also the flexibility that such access provides, as Piyush Zutshi, director of RMS Analytical Services points out. “That creates a huge value-add in terms of our catastrophe response capabilities — knowing that we are able to report our latest position has made a big difference on this front” Judith Woo Starstone “In those initial years, companies often require the flexibility of an outsourced modeling capability, as there is a degree of uncertainty at that stage regarding potential growth rates and the possibility that they may change track and consider alternative lines of business or territories should other areas not prove as profitable as predicted.” Another big outsourcing driver is the potential to free up valuable internal expertise, as Sehdev explains. “Often, the daily churn of data processing consumes a huge amount of internal analytical resources,” she says, “and limits the opportunities for these highly skilled experts to devote sufficient time to analyzing the data output and supporting the decision-making process.” This all-too-common data stumbling block for many companies is one that not only affects their ability to capitalize fully on their data, but also to retain key analytical staff. “Companies hire highly skilled analysts to boost their data performance,” Zutshi says, “but most of their working day is taken up by data crunching. That makes it extremely challenging to retain that caliber of staff as they are massively overqualified for the role and also have limited potential for career growth.” Other reasons for outsourcing include new model testing. It provides organizations with a sandbox testing environment to assess the potential benefits and impact of a new model on their underwriting processes and portfolio management capabilities before committing to the license fee. The flexibility of outsourced model capabilities can also prove critical during renewal periods. These seasonal activity peaks can be factored into contracts to ensure that organizations are able to cope with the spike in data analysis required as they reanalyze portfolios, renew contracts, add new business and write off old business. “At RMS Analytical Services,” Zutshi explains, “we prepare for data surge points well in advance. We work with clients to understand the potential size of the analytical spike, and then we add a factor of 20 to 30 percent to that to ensure that we have the data processing power on hand should that surge prove greater than expected.” Things to Consider Integrating an outsourced function into existing modeling processes can prove a demanding undertaking, particularly in the early stages where companies will be required to commit time and resources to the knowledge transfer required to ensure a seamless integration. The structure of the existing infrastructure will, of course, be a major influencing factor in the ease of transition. “There are those companies that over the years have invested heavily in their in-house capabilities and developed their own systems that are very tightly bound within their processes,” Sehdev points out, “which can mean decoupling certain aspects is more challenging. For those operations that run much leaner infrastructures, it can often be more straightforward to decouple particular components of the processing.” RMS Analytical Services has, however, addressed this issue and now works increasingly within the systems of such clients, rather than operating as an external function. “We have the ability to work remotely, which means our teams operate fully within their existing framework. This removes the need to decouple any parts of the data chain, and we can fit seamlessly into their processes.” This also helps address any potential data transfer issues companies may have, particularly given increasingly stringent information management legislation and guidelines. There are a number of factors that will influence the extent to which a company will outsource its modeling function. Unsurprisingly, smaller organizations and startup operations are more likely to take the fully outsourced option, while larger companies tend to use it as a means of augmenting internal teams — particularly around data engineering. RMS Analytical Services operate various different engagement models. Managed services are based on annual contracts governed by volume for data engineering and risk analytics. On-demand services are available for one-off risk analytics projects, renewals support, bespoke analysis such as event response, and new IP adoption. “Modeler down the hall” is a third option that provides ad hoc work, while the firm also offers consulting services around areas such as process optimization, model assessment and transition support. Making the Transition Work Starstone Insurance, a global specialty insurer providing a diversified range of property, casualty and specialty insurance to customers worldwide, has been operating an outsourced modeling function for two and a half years. “My predecessor was responsible for introducing the outsourced component of our modeling operations,” explains Judith Woo, head of exposure management at Starstone. “It was very much a cost-driven decision as outsourcing can provide a very cost-effective model.” The company operates a hybrid model, with the outsourced team working on most of the pre- and post-bind data processing, while its internal modeling team focuses on the complex specialty risks that fall within its underwriting remit. “The volume of business has increased over the years as has the quality of data we receive,” she explains. “The amount of information we receive from our brokers has grown significantly. A lot of the data processing involved can be automated and that allows us to transfer much of this work to RMS Analytical Services.” On a day-to-day basis, the process is straightforward, with the Starstone team uploading the data to be processed via the RMS data portal. The facility also acts as a messaging function with the two teams communicating directly. “In fact,” Woo points out, “there are email conversations that take place directly between our underwriters and the RMS Analytical Service team that do not always require our modeling division’s input.” However, reaching this level of integration and trust has required a strong commitment from Starstone to making the relationship work. “You are starting to work with a third-party operation that does not understand your business or its data processes. You must invest time and energy to go through the various systems and processes in detail,” she adds, “and that can take months depending on the complexity of the business. “You are essentially building an extension of your team, and you have to commit to making that integration work. You can’t simply bring them in, give them a particular problem and expect them to solve it without there being the necessary knowledge transfer and sharing of information.” Her internal modeling team of six has access to an outsourced team of 26, she explains, which greatly enhances the firm’s data-handling capabilities. “With such a team, you can import fresh data into the modeling process on a much more frequent basis, for example. That creates a huge value-add in terms of our catastrophe response capabilities — knowing that we are able to report our latest position has made a big difference on this front.” Creating a Partnership As with any working partnership, the initial phases are critical as they set the tone for the ongoing relationship. “We have well-defined due diligence and transition methodologies,” Zutshi states. “During the initial phase, we work to understand and evaluate their processes. We then create a detailed transition methodology, in which we define specific data templates, establish monthly volume loads, lean periods and surge points, and put in place communication and reporting protocols.” At the end, both parties have a full documented data dictionary with business rules governing how data will be managed, coupled with the option to choose from a repository of 1,000+ validation rules for data engineering. This is reviewed on a regular basis to ensure all processes remain aligned with the practices and direction of the organization. “Often, the daily churn of data processing consumes a huge amount of internal analytical resources and limits the opportunities to devote sufficient time to analyzing the data output” — Neetika Kapoor Sehdev, RMS Service level agreements (SLAs) also form also form a central tenet of the relationship plus stringent data compliance procedures. “Robust data security and storage is critical,” says Woo. “We have comprehensive NDAs [non-disclosure agreements] in place that are GDPR  compliant to ensure that the integrity of our data is maintained throughout. We also have stringent SLAs in place to guarantee data processing turnaround times. Although, you need to agree on a reasonable time period reflecting the data complexity and also when it is delivered.” According to Sehdev, most SLAs that the analytical team operates require a 24-hour data turnaround rising to 48-72 hours for more complex data requirements, but clients are able to set priorities as needed. “However, there is no point delivering on turnaround times,” she adds, “if the quality of the data supplied is not fit for purpose. That’s why we apply a number of data quality assurance processes, which means that our first-time accuracy level is over 98 percent.” The Value-Add Most clients of RMS Analytical Services have outsourced modeling functions to the division for over seven years, with a number having worked with the team since it launched in 2004. The decision to incorporate their services is not taken lightly given the nature of the information involved and the level of confidence required in their capabilities. “The majority of our large clients bring us on board initially in a data-engineering capacity,” explains Sehdev. “It’s the building of trust and confidence in our ability, however, that helps them move to the next tranche of services.” The team has worked to strengthen and mature these relationships, which has enabled them to increase both the size and scope of the engagements they undertake. “With a number of clients, our role has expanded to encompass account modeling, portfolio roll-up and related consulting services,” says Zutshi. “Central to this maturing process is that we are interacting with them daily and have a dedicated team that acts as the primary touch point. We’re also working directly with the underwriters, which helps boost comfort and confidence levels. “For an outsourced model function to become an integral part of the client’s team,” he concludes, “it must be a close, coordinated effort between the parties. That’s what helps us evolve from a standard vendor relationship to a trusted partner.”

NIGEL ALLEN
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September 05, 2018
Making it Clear

Pete Dailey of RMS explains why model transparency is critical to client confidence View of Hurricane Harvey from space In the aftermath of Hurricances Harvey, Irma and Maria (HIM), there was much comment on the disparity among the loss estimates produced by model vendors. Concerns have been raised about significant outlier results released by some modelers. “It’s no surprise,” explains Dr. Pete Dailey, vice president at RMS, “that vendors who approach the modeling differently will generate different estimates. But rather than pushing back against this, we feel it’s critical to acknowledge and understand these differences. “At RMS, we develop probabilistic models that operate across the full model space and deliver that insight to our clients. Uncertainty is inherent within the modeling process for any natural hazard, so we can’t rely solely on past events, but rather simulate the full range of plausible future events.” There are multiple components that contribute to differences in loss estimates, including the scientific approaches and technologies used and the granularity of the exposure data. “Increased demand for more immediate data is encouraging modelers to push the envelope” “As modelers, we must be fully transparent in our loss-estimation approach,” he states. “All apply scientific and engineering knowledge to detailed exposure data sets to generate the best possible estimates given the skill of the model. Yet the models always provide a range of opinion when events happen, and sometimes that is wider than expected. Clients must know exactly what steps we take, what data we rely upon, and how we apply the models to produce our estimates as events unfold. Only then can stakeholders conduct the due diligence to effectively understand the reasons for the differences and make important financial decisions accordingly.” Outlier estimates must also be scrutinized in greater detail. “There were some outlier results during HIM, and particularly for Hurricane Maria. The onus is on the individual modeler to acknowledge the disparity and be fully transparent about the factors that contributed to it. And most importantly, how such disparity is being addressed going forward,” says Dailey. “A ‘big miss’ in a modeled loss estimate generates market disruption, and without clear explanation this impacts the credibility of all catastrophe models. RMS models performed quite well for Maria. One reason for this was our detailed local knowledge of the building stock and engineering practices in Puerto Rico. We’ve built strong relationships over the years and made multiple visits to the island, and the payoff for us and our client comes when events like Maria happen.” As client demand for real-time and pre-event estimates grows, the data challenge placed on modelers is increasing. “Demand for more immediate data is encouraging modelers like RMS to push the scientific envelope,” explains Dailey, “as it should. However, we need to ensure all modelers acknowledge, and to the degree possible quantify, the difficulties inherent in real-time loss estimation — especially since it’s often not possible to get eyes on the ground for days or weeks after a major catastrophe.” Much has been said about the need for modelers to revise initial estimates months after an event occurs. Dailey acknowledges that while RMS sometimes updates its estimates, during HIM the strength of early estimates was clear. “In the months following HIM, we didn’t need to significantly revise our initial loss figures even though they were produced when uncertainty levels were at their peak as the storms unfolded in real time,” he states. “The estimates for all three storms were sufficiently robust in the immediate aftermath to stand the test of time. While no one knows what the next event will bring, we’re confident our models and, more importantly, our transparent approach to explaining our estimates will continue to build client confidence.”

Helen Yates
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September 05, 2018
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. 

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