The insurance industry has reached a transformational point in its ability to accurately understand the details of exposure at risk. It is the point at which three fundamental components of exposure management are coming together to enable (re)insurers to systematically quantify risk at the location level: the availability of high-resolution location data, access to the technology to capture that data and advances in modeling capabilities to use that data. Data resolution at the individual building level has increased considerably in recent years, including the use of detailed satellite imagery, while advances in data sourcing technology have provided companies with easier access to this more granular information. In parallel, the evolution of new innovations, such as RMS® High Definition Models™ and the transition to cloud-based technologies, has facilitated a massive leap forward in the ability of companies to absorb, analyze and apply this new data within their actuarial and underwriting ecosystems. Quantifying Risk Uncertainty “Risk has an inherent level of uncertainty,” explains Mohsen Rahnama, chief modeling officer at RMS. “The key is how you quantify that uncertainty. No matter what hazard you are modeling, whether it is earthquake, flood, wildfire or hurricane, there are assumptions being made. These catastrophic perils are low-probability, high-consequence events as evidenced, for example, by the 2017 and 2018 California wildfires or Hurricane Katrina in 2005 and Hurricane Harvey in 2017. For earthquake, examples include Tohoku in 2011, the New Zealand earthquakes in 2010 and 2011, and Northridge in 1994. For this reason, risk estimation based on an actuarial approach cannot be carried out for these severe perils; physical models based upon scientific research and event characteristic data for estimating risk are needed.” A critical element in reducing uncertainty is a clear understanding of the sources of uncertainty from the hazard, vulnerability and exposure at risk. “Physical models, such as those using a high-definition approach, systematically address and quantify the uncertainties associated with the hazard and vulnerability components of the model,” adds Rahnama. “There are significant epistemic (also known as systematic) uncertainties in the loss results, which users should consider in their decision-making process. This epistemic uncertainty is associated with a lack of knowledge. It can be subjective and is reducible with additional information.” What are the sources of this uncertainty? For earthquake, there is uncertainty about the ground motion attenuation functions, soil and geotechnical data, the size of the events, or unknown faults. Rahnama explains: “Addressing the modeling uncertainty is one side of the equation. Computational power enables millions of events and more than 50,000 years of simulation to be used, to accurately capture the hazard and reduce the epistemic uncertainty. Our findings show that in the case of earthquakes the main source of uncertainty for portfolio analysis is ground motion; however, vulnerability is the main driver of uncertainty for a single location.” The quality of the exposure data as the input to any mathematical models is essential to assess the risk accurately and reduce the loss uncertainty. However, exposure could represent the main source of loss uncertainty, especially when exposure data is provided in aggregate form. Assumptions can be made to disaggregate exposure using other sources of information, which helps to some degree reduce the associated uncertainty. Rahnama concludes, “Therefore, it is essential in order to minimize the uncertainty related to exposure to try to get location-level information about the exposure, in particular for the region with the potential of liquification for earthquake or for high-gradient hazard such as flood and wildfire.” A critical element in reducing that uncertainty, removing those assumptions and enhancing risk understanding is combining location-level data and hazard information. That combination provides the data basis for quantifying risk in a systematic way. Understanding the direct correlation between risk or hazard and exposure requires location-level data. The potential damage caused to a location by flood, earthquake or wind will be significantly influenced by factors such as first-floor elevation of a building, distance to fault lines or underlying soil conditions through to the quality of local building codes and structural resilience. And much of that granular data is now available and relatively easy to access. “The amount of location data that is available today is truly phenomenal,” believes Michael Young, vice president of product management at RMS, “and so much can be accessed through capabilities as widely available as Google Earth. Straightforward access to this highly detailed satellite imagery means that you can conduct desktop analysis of individual properties and get a pretty good understanding of many of the building and location characteristics that can influence exposure potential to perils such as wildfire.” Satellite imagery is already a core component of RMS model capabilities, and by applying machine learning and artificial intelligence (AI) technologies to such images, damage quantification and differentiation at the building level is becoming a much more efficient and faster undertaking — as demonstrated in the aftermath of Hurricanes Laura and Delta. “Within two days of Hurricane Laura striking Louisiana at the end of August 2020,” says Rahnama, “we had been able to assess roof damage to over 180,000 properties by applying our machine-learning capabilities to satellite images of the affected areas. We have ‘trained’ our algorithms to understand damage degree variations and can then superimpose wind speed and event footprint specifics to group the damage degrees into different wind speed ranges. What that also meant was that when Hurricane Delta struck the same region weeks later, we were able to see where damage from these two events overlapped.” The Data Intensity of Wildfire Wildfire by its very nature is a data-intensive peril, and the risk has a steep gradient where houses in the same neighborhood can have drastically different risk profiles. The range of factors that can make the difference between total loss, partial loss and zero loss is considerable, and to fully grasp their influence on exposure potential requires location-level data. The demand for high-resolution data has increased exponentially in the aftermath of recent record-breaking wildfire events, such as the series of devastating seasons in California in 2017-18, and unparalleled bushfire losses in Australia in 2019-20. Such events have also highlighted myriad deficiencies in wildfire risk assessment including the failure to account for structural vulnerabilities, the inability to assess exposure to urban conflagrations, insufficient high-resolution data and the lack of a robust modeling solution to provide insight about fire potential given the many years of drought. Wildfires in 2017 devastated the town of Paradise, California In 2019, RMS released its U.S. Wildfire HD Model, built to capture the full impact of wildfire at high resolution, including the complex behaviors that characterize fire spread, ember accumulation and smoke dispersion. Able to simulate over 72 million wildfires across the contiguous U.S., the model creates ultrarealistic fire footprints that encompass surface fuels, topography, weather conditions, moisture and fire suppression measures. “To understand the loss potential of this incredibly nuanced and multifactorial exposure,” explains Michael Young, “you not only need to understand the probability of a fire starting but also the probability of an individual building surviving. “If you look at many wildfire footprints,” he continues, “you will see that sometimes up to 60 percent of buildings within that footprint survived, and the focus is then on what increases survivability — defensible space, building materials, vegetation management, etc. We were one of the first modelers to build mitigation factors into our model, such as those building and location attributes that can enhance building resilience.” Moving the Differentiation Needle In a recent study by RMS and the Center for Insurance Policy Research, the Insurance Institute for Business and Home Safety and the National Fire Protection Association, RMS applied its wildfire model to quantifying the benefits of two mitigation strategies — structural mitigation and vegetation management — assessing hypothetical loss reduction benefits in nine communities across California, Colorado and Oregon. Young says: “By knowing what the building characteristics and protection measures are within the first 5 feet and 30 feet at a given property, we were able to demonstrate that structural modifications can reduce wildfire risk up to 35 percent, while structural and vegetation modifications combined can reduce it by up to 75 percent. This level of resolution can move the needle on the availability of wildfire insurance as it enables development of robust rating algorithms to differentiate specific locations — and means that entire neighborhoods don’t have to be non-renewed.” “By knowing what the building characteristics and protection measures are within the first 5 feet and 30 feet at a given property, we were able to demonstrate that structural modifications can reduce wildfire risk up to 35 percent, while structural and vegetation modifications combined can reduce it by up to 75 percent” Michael Young, RMS While acknowledging that modeling mitigation measures at a 5-foot resolution requires an immense granularity of data, RMS has demonstrated that its wildfire model is responsive to data at that level. “The native resolution of our model is 50-meter cells, which is a considerable enhancement on the zip-code level underwriting grids employed by some insurers. That cell size in a typical suburban neighborhood encompasses approximately three-to-five buildings. By providing the model environment that can utilize information within the 5-to-30-foot range, we are enabling our clients to achieve the level of data fidelity to differentiate risks at that property level. That really is a potential market game changer.” Evolving Insurance Pricing It is not hyperbolic to suggest that being able to combine high-definition modeling with high-resolution data can be market changing. The evolution of risk-based pricing in New Zealand is a case in point. The series of catastrophic earthquakes in the Christchurch region of New Zealand in 2010 and 2011 provided a stark demonstration of how insufficient data meant that the insurance market was blindsided by the scale of liquefaction-related losses from those events. “The earthquakes showed that the market needed to get a lot smarter in how it approached earthquake risk,” says Michael Drayton, consultant at RMS, “and invest much more in understanding how individual building characteristics and location data influenced exposure performance, particularly in relation to liquefaction. “To get to grips with this component of the earthquake peril, you need location-level data,” he continues. “To understand what triggers liquefaction, you must analyze the soil profile, which is far from homogenous. Christchurch, for example, sits on an alluvial plain, which means there are multiple complex layers of silt, gravel and sand that can vary significantly from one location to the next. In fact, across a large commercial or industrial complex, the soil structure can change significantly from one side of the building footprint to the other.” Extensive building damage in downtown Christchurch, New Zealand after 2011 earthquake The aftermath of the earthquake series saw a surge in soil data as teams of geotech engineers conducted painstaking analysis of layer composition. With multiple event sets to use, it was possible to assess which areas suffered soil liquefaction and from which specific ground-shaking intensity. “Updating our model with this detailed location information brought about a step-change in assessing liquefaction exposures. Previously, insurers could only assess average liquefaction exposure levels, which was of little use where you have highly concentrated risks in specific areas. Through our RMS® New Zealand Earthquake HD Model, which incorporates 100-meter grid resolution and the application of detailed ground data, it is now possible to assess liquefaction exposure potential at a much more localized level.” “Through our RMS® New Zealand Earthquake HD model, which incorporates 100-meter grid resolution and the application of detailed ground data, it is now possible to assess liquefaction exposure potential at a much more localized level” — Michael Drayton, RMS This development represents a notable market shift from community to risk-based pricing in New Zealand. With insurers able to differentiate risks at the location level, this has enabled companies such as Tower Insurance to more accurately adjust premium levels to reflect risk to the individual property or area. In its annual report in November 2019, Tower stated: “Tower led the way 18 months ago with risk-based pricing and removing cross-subsidization between low- and high-risk customers. Risk-based pricing has resulted in the growth of Tower’s portfolio in Auckland while also reducing exposure to high-risk areas by 16 percent. Tower’s fairer approach to pricing has also allowed the company to grow exposure by 4 percent in the larger, low-risk areas like Auckland, Hamilton, and Taranaki.” Creating the Right Ecosystem The RMS commitment to enable companies to put high-resolution data to both underwriting and portfolio management use goes beyond the development of HD Models™ and the integration of multiple layers of location-level data. Through the launch of RMS Risk Intelligence™, its modular, unified risk analytics platform, and the Risk Modeler™ application, which enables users to access, evaluate, compare and deploy all RMS models, the company has created an ecosystem built to support these next-generation data capabilities. Deployed within the Cloud, the ecosystem thrives on the computational power that this provides, enabling proprietary and tertiary data analytics to rapidly produce high-resolution risk insights. A network of applications — including the ExposureIQ™ and SiteIQ™ applications and Location Intelligence API — support enhanced access to data and provide a more modular framework to deliver that data in a much more customized way. “Because we are maintaining this ecosystem in the Cloud,” explains Michael Young, “when a model update is released, we can instantly stand that model side-by-side with the previous version. As more data becomes available each season, we can upload that new information much faster into our model environment, which means our clients can capitalize on and apply that new insight straightaway.” Michael Drayton adds: “We’re also offering access to our capabilities in a much more modular fashion, which means that individual teams can access the specific applications they need, while all operating in a data-consistent environment. And the fact that this can all be driven through APIs means that we are opening up many new lines of thought around how clients can use location data.” Exploring What Is Possible There is no doubt that the market is on the cusp of a new era of data resolution — capturing detailed hazard and exposure and using the power of analytics to quantify the risk and risk differentiation. Mohsen Rahnama believes the potential is huge. “I foresee a point in the future where virtually every building will essentially have its own social-security-like number,” he believes, “that enables you to access key data points for that particular property and the surrounding location. It will effectively be a risk score, including data on building characteristics, proximity to fault lines, level of elevation, previous loss history, etc. Armed with that information — and superimposing other data sources such as hazard data, geological data and vegetation data — a company will be able to systematically price risk and assess exposure levels for every asset up to the portfolio level.” “The only way we can truly assess this rapidly changing risk is by being able to systematically evaluate exposure based on high-resolution data and advanced modeling techniques that incorporate building resilience and mitigation measures” — Mohsen Rahnama, RMS Bringing the focus back to the here and now, he adds, the expanding impacts of climate change are making the need for this data transformation a market imperative. “If you look at how many properties around the globe are located just one meter above sea level, we are talking about trillions of dollars of exposure. The only way we can truly assess this rapidly changing risk is by being able to systematically evaluate exposure based on high-resolution data and advanced modeling techniques that incorporate building resilience and mitigation measures. How will our exposure landscape look in 2050? The only way we will know is by applying that data resolution underpinned by the latest model science to quantify this evolving risk.”
Karen White joined RMS as CEO in March 2018, followed closely by Moe Khosravy, general manager of software and platform activities. EXPOSURE talks to both, along with Mohsen Rahnama, chief risk modeling officer and one of the firm’s most long-standing team members, about their collective vision for the company, innovation, transformation and technology in risk management Karen and Moe, what was it that sparked your interest in joining RMS? Karen: What initially got me excited was the strength of the hand we have to play here and the fact that the insurance sector is at a very interesting time in its evolution. The team is fantastic — one of the most extraordinary groups of talent I have come across. At our core, we have hundreds of Ph.D.s, superb modelers and scientists, surrounded by top engineers, and computer and data scientists. I firmly believe no other modeling firm holds a candle to the quality of leadership and depth and breadth of intellectual property at RMS. We are years ahead of our competitors in terms of the products we deliver. Moe: For me, what can I say? When Karen calls with an idea it’s very hard to say no! However, when she called about the RMS opportunity, I hadn’t ever considered working in the insurance sector. My eureka moment came when I looked at the industry’s challenges and the technology available to tackle them. I realized that this wasn’t simply a cat modeling property insurance play, but was much more expansive. If you generalize the notion of risk and loss, the potential of what we are working on and the value to the insurance sector becomes much greater. I thought about the technologies entering the sector and how new developments on the AI [artificial intelligence] and machine learning front could vastly expand current analytical capabilities. I also began to consider how such technologies could transform the sector’s cost base. In the end, the decision to join RMS was pretty straightforward. “Developments such as AI and machine learning are not fairy dust to sprinkle on the industry’s problems” Karen White CEO, RMS Karen: The industry itself is reaching a eureka moment, which is precisely where I love to be. It is at a transformational tipping point — the technology is available to enable this transformation and the industry is compelled to undertake it. I’ve always sought to enter markets at this critical point. When I joined Oracle in the 1990s, the business world was at a transformational point — moving from client-server computing to Internet computing. This has brought about many of the huge changes we have seen in business infrastructure since, so I had a bird’s-eye view of what was a truly extraordinary market shift coupled with a technology shift. That experience made me realize how an architectural shift coupled with a market shift can create immense forward momentum. If the technology can’t support the vision, or if the challenges or opportunities aren’t compelling enough, then you won’t see that level of change occur. Do (re)insurers recognize the need to change and are they willing to make the digital transition required? Karen: I absolutely think so. There are incredible market pressures to become more efficient, assess risks more effectively, improve loss ratios, achieve better business outcomes and introduce more beneficial ways of capitalizing risk. You also have numerous new opportunities emerging. New perils, new products and new ways of delivering those products that have huge potential to fuel growth. These can be accelerated not just by market dynamics but also by a smart embrace of new technologies and digital transformation. Mohsen: Twenty-five years ago when we began building models at RMS, practitioners simply had no effective means of assessing risk. So, the adoption of model technology was a relatively simple step. Today, the extreme levels of competition are making the ability to differentiate risk at a much more granular level a critical factor, and our model advances are enabling that. In tandem, many of the Silicon Valley technologies have the potential to greatly enhance efficiency, improve processing power, minimize cost, boost speed to market, enable the development of new products, and positively impact every part of the insurance workflow. Data is the primary asset of our industry — it is the source of every risk decision, and every risk is itself an opportunity. The amount of data is increasing exponentially, and we can now capture more information much faster than ever before, and analyze it with much greater accuracy to enable better decisions. It is clear that the potential is there to change our industry in a positive way. The industry is renowned for being risk averse. Is it ready to adopt the new technologies that this transformation requires? Karen: The risk of doing nothing given current market and technology developments is far greater than that of embracing emerging tech to enable new opportunities and improve cost structures, even though there are bound to be some bumps in the road. I understand the change management can be daunting. But many of the technologies RMS is leveraging to help clients improve price performance and model execution are not new. AI, the Cloud and machine learning are already tried and trusted, and the insurance market will benefit from the lessons other industries have learned as it integrates these technologies. “The sector is not yet attracting the kind of talent that is attracted to firms such as Google, Microsoft or Amazon — and it needs to” Moe Khosravy EVP, Software and Platform, RMS Moe: Making the necessary changes will challenge the perceived risk-averse nature of the insurance market as it will require new ground to be broken. However, if we can clearly show how these capabilities can help companies be measurably more productive and achieve demonstrable business gains, then the market will be more receptive to new user experiences. Mohsen: The performance gains that technology is introducing are immense. A few years ago, we were using computation fluid dynamics to model storm surge. We were conducting the analysis through CPU [central processing unit] microprocessors, which was taking weeks. With the advent of GPU [graphics processing unit] microprocessors, we can carry out the same level of analysis in hours. When you add the supercomputing capabilities possible in the Cloud, which has enabled us to deliver HD-resolution models to our clients — in particular for flood, which requires a high-gradient hazard model to differentiate risk effectively — it has enhanced productivity significantly and in tandem price performance. Is an industry used to incremental change able to accept the stepwise change technology can introduce? Karen: Radical change often happens in increments. The change from client-server to Internet computing did not happen overnight, but was an incremental change that came in waves and enabled powerful market shifts. Amazon is a good example of market leadership out of digital transformation. It launched in 1994 as an online bookstore in a mature, relatively sleepy industry. It evolved into broad e-commerce and again with the introduction of Cloud services when it launched AWS [Amazon Web Services] 12 years ago — now a US$17 billion business that has disrupted the computer industry and is a huge portion of its profit. Amazon has total revenue of US$178 billion from nothing over 25 years, having disrupted the retail sector. Retail consumption has changed dramatically, but I can still go shopping on London’s Oxford Street and about 90 percent of retail is still offline. My point is, things do change incrementally but standing still is not a great option when technology-fueled market dynamics are underway. Getting out in front can be enormously rewarding and create new leadership. However, we must recognize that how we introduce technology must be driven by the challenges it is being introduced to address. I am already hearing people talk about developments such as AI, machine learning and neural networks as if they are fairy dust to sprinkle on the industry’s problems. That is not how this transformation process works. How are you approaching the challenges that this transformation poses? Karen: At RMS, we start by understanding the challenges and opportunities from our customers’ perspectives and then look at what value we can bring that we have not brought before. Only then can we look at how we deliver the required solution. Moe: It’s about having an “outward-in” perspective. We have amazing technology expertise across modeling, computer science and data science, but to deploy that effectively we must listen to what the market wants. We know that many companies are operating multiple disparate systems within their networks that have simply been built upon again and again. So, we must look at harnessing technology to change that, because where you have islands of data, applications and analysis, you lose fidelity, time and insight and costs rise. Moe: While there is a commonality of purpose spanning insurers, reinsurers and brokers, every organization is different. At RMS, we must incorporate that into our software and our platforms. There is no one-size-fits-all and we can’t force everyone to go down the same analytical path. That’s why we are adopting a more modular approach in terms of our software. Whether the focus is portfolio management or underwriting decision-making, it’s about choosing those modules that best meet your needs. “Data is the primary asset of our industry — it is the source of every risk decision, and every risk is itself an opportunity” Mohsen Rahmana, PhD Chief Risk Modeling Officer, RMS Mohsen: When constructing models, we focus on how we can bring the right technology to solve the specific problems our clients have. This requires a huge amount of critical thinking to bring the best solution to market. How strong is the talent base that is helping to deliver this level of capability? Mohsen: RMS is extremely fortunate to have such a fantastic array of talent. This caliber of expertise is what helps set us apart from competitors, enabling us to push boundaries and advance our modeling capabilities at the speed we are. Recently, we have set up teams of modelers and data and computer scientists tasked with developing a range of innovations. It’s fantastic having this depth of talent, and when you create an environment in which innovative minds can thrive you quickly reap the rewards — and that is what we are seeing. In fact, I have seen more innovation at RMS in the last six months than over the past several years. Moe: I would add though that the sector is not yet attracting the kind of talent seen at firms such as Google, Microsoft or Amazon, and it needs to. These companies are either large-scale customer-service providers capitalizing on big data platforms and leading-edge machine-learning techniques to achieve the scale, simplicity and flexibility their customers demand, or enterprises actually building these core platforms themselves. When you bring new blood into an organization or industry, you generate new ideas that challenge current thinking and practices, from the user interface to the underlying platform or the cost of performance. We need to do a better PR job as a technology sector. The best and brightest people in most cases just want the greatest problems to tackle — and we have a ton of those in our industry. Karen: The critical component of any successful team is a balance of complementary skills and capabilities focused on having a high impact on an interesting set of challenges. If you get that dynamic right, then that combination of different lenses correctly aligned brings real clarity to what you are trying to achieve and how to achieve it. I firmly believe at RMS we have that balance. If you look at the skills, experience and backgrounds of Moe, Mohsen and myself, for example, they couldn’t be more different. Bringing Moe and Mohsen together, however, has quickly sparked great and different thinking. They work incredibly well together despite their vastly different technical focus and career paths. In fact, we refer to them as the “Moe-Moes” and made them matching inscribed giant chain necklaces and presented them at an all-hands meeting recently. Moe: Some of the ideas we generate during our discussions and with other members of the modeling team are incredibly powerful. What’s possible here at RMS we would never have been able to even consider before we started working together. Mohsen: Moe’s vast experience of building platforms at companies such as HP, Intel and Microsoft is a great addition to our capabilities. Karen brings a history of innovation and building market platforms with the discipline and the focus we need to deliver on the vision we are creating. If you look at the huge amount we have been able to achieve in the months that she has been at RMS, that is a testament to the clear direction we now have. Karen: While we do come from very different backgrounds, we share a very well-defined culture. We care deeply about our clients and their needs. We challenge ourselves every day to innovate to meet those needs, while at the same time maintaining a hell-bent pragmatism to ensure we deliver. Mohsen: To achieve what we have set out to achieve requires harmony. It requires a clear vision, the scientific know-how, the drive to learn more, the ability to innovate and the technology to deliver — all working in harmony. Career Highlights Karen White is an accomplished leader in the technology industry, with a 25-year track record of leading, innovating and scaling global technology businesses. She started her career in Silicon Valley in 1993 as a senior executive at Oracle. Most recently, Karen was president and COO at Addepar, a leading fintech company serving the investment management industry with data and analytics solutions. Moe Khosravy (center) has over 20 years of software innovation experience delivering enterprise-grade products and platforms differentiated by data science, powerful analytics and applied machine learning to help transform industries. Most recently he was vice president of software at HP Inc., supporting hundreds of millions of connected devices and clients. Mohsen Rahnama leads a global team of accomplished scientists, engineers and product managers responsible for the development and delivery of all RMS catastrophe models and data. During his 20 years at RMS, he has been a dedicated, hands-on leader of the largest team of catastrophe modeling professionals in the industry.