Category Archives: Natural Catastrophe Risk

RMS HWind Forecasting: Untangling the Spaghetti

Every twist and turn of a real-time hurricane can affect global financial markets, public safety, or government and international aid agencies that provide assistance. Within the (re)insurance space, the ability to understand forecast track, timing, and potential hazard and loss impacts before landfall helps entities to prepare and execute their event response processes effectively. This includes having adequate capital to cover claims, setting up claim centers and planning policyholder outreach, securing and positioning adjusters in areas that are likely to be impacted, and determining what, if any, risk can be ceded to reinsurance or capital markets.

For our clients, the traditional approach to quantify potential impacts ahead of a landfalling storm involves selecting similar storms from the RMS® North Atlantic Hurricane (NAHU) stochastic event set. While this generates vital insights that can be extracted quickly from internal databases, there are opportunities to provide earlier and more comprehensive insights into the storm ahead of landfall. 

To date, RMS clients have also benefited from real-time analysis of hurricane events through RMS HWind Real-Time Analysis products. These observation data-based snapshots and footprints have provided the industry with a standard “ground truth” representation of tropical cyclone wind field size and intensity before, during, and following landfall effectively helping to describe what the storm is doing and what the storm has done.

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Typhoon Lekima Follows a Familiar Path

The first half of 2019 had been unusually quiet in the western North Pacific tropical cyclone basin. Following the dissipation of the strongest-ever February typhoon – Wutip, there were no subsequent typhoons until Francisco reached Category 1 strength on August 4. A few days later, Typhoon Lekima strengthened significantly on its approach towards the China coastline and then became the strongest landfalling storm of the year so far.

Lekima Enters the Record Books

Typhoon Lekima made landfall in Wenling City, Zhejiang Province (pop. ~1.3 million), at 1:45 a.m. local time on Saturday, August 10, with an intensity equivalent to a Category 3 hurricane on the Saffir-Simpson Hurricane Wind Scale according to the China Meteorological Administration (CMA). With two-minute sustained winds of 116 miles per hour (187 kilometers per hour) and a central pressure at landfall of 930 millibars, Lekima became the third strongest tropical cyclone to impact eastern China after Saomai in 2006 and Wanda in 1956.

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Finding the City That Holds Your Climate Future

Imagine, instead of trying to communicate the prospective climate change future, you could just time travel to experience the weather of 2050.

In place of having to convince the city engineers of Paris or Chicago to invest in better street drainage and passive-cooling architecture, you could take them to experience their city in thirty years, well within the lifetime of the facilities and infrastructure they are constructing today. Rather than having to factor in seemingly arbitrary modifiers to flood or heatwave risks, to stress test your future insurance losses, you could visit an insurer already experiencing and pricing those future climate extremes.

In evaluating climate, we already have an alternative to time travel – we can travel in latitude. You could accomplish all these tangible goals, if you could identify the place which today already experiences your future climate.    

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Climate Change – What’s the GIST?

From our numerous client conversations, climate change as a business issue has risen high on the agenda, and this has certainly escalated over the last twelve months. There is a growing recognition of the need to quantify the impact that climate change will have on your business. But – where do you start with this? One of the major challenges is knowing what question to ask. With the inclusion of climate change scenarios within the General Insurance Stress Test (GIST 2019), which the larger U.K. insurers and Lloyd’s syndicates are required to respond to, the Bank of England Prudential Regulation Authority (PRA) is outlining one approach.

RMS is particularly well placed to support insurers in responding to the “Assumptions to Assess the Impact on an Insurer’s Liabilities” portion of the climate change section within GIST, which examines how changes in U.S. hurricane and U.K. weather risk under different climate change scenarios may affect losses.

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Should New Zealand Be Content with the New EQC Coverages and Caps?

The revised earthquake coverages and caps proposed by the New Zealand Earthquake Commission (EQC) came into law as planned on July 1, 2019. As noted in an RMS blog back in February, these well signaled changes – to increase the building coverage from NZ$100,000 to NZ$150,000 and remove the NZ$20,000 contents cover, only had a small effect on the gross average annual loss for both EQC and the private market. Swapping the first layer of contents exposure for a larger, higher layer of building exposure produced a result that was close to neutral.

Examining the Exceedance Probability (EP) curve (see figure 1 below), the changes are small across all return periods. There are small increases in loss for the private market at short return periods (which produce the small increase in average annual loss reported earlier) but very little change at long return periods.

Critically, the modeled gross 1000-year loss to the private market is essentially unchanged, meaning there are no implications with regards to the Reserve Bank of New Zealand (RBNZ) solvency requirements. Further, these EQC coverage changes are not expected to affect the peril balance driving trans-Tasman solvency considerations where both the RBNZ and Australian Prudential Regulation Authority (APRA) standards must be met.

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The Mw7.1 – 2019 Ridgecrest (California) Earthquake Sequence

The recent events that shook a relatively remote part of the Mojave Desert region of Eastern California provide an acute reminder of the major risk posed by earthquakes in the state. It has been a while now since California experienced a large earthquake, and the main event in this sequence – with a magnitude of Mw7.1, was the most powerful earthquake to occur in the state in twenty years.

Since then, the field of seismology as well as earth scientific measuring capabilities have undergone quite substantial improvements and innovations. Immediately after the start of the sequence, several coordinated efforts from academic, government and engineering organizations resulted in focused field surveys and the installation of additional, more densely spaced instrumentation to monitor seismicity and surface deformation, in and around the epicentral area.

So far, extraordinary amounts of high-quality data have been collected that will undoubtedly provide new insights and understanding of earthquakes in general and earthquake hazard and risk in (Southern) California, in particular. Work on these new data sets has only just started, but what have we learned so far? Here is a summary of observations and interpretations based on various (preliminary) field surveys, reports and briefings.

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Risk Intelligence 1.12 Release … and How to Democratize Your Data Using a JDBC/ODBC Connector

We are pleased to announce that RMS Risk Intelligence™ version 1.12 has now been released. This new release includes many improvements such as the introduction of new Structure Tags and Domain Data Tables related to items such as “Line of Business”, “Underwriting Group” and “Offer Type”. The release also includes an assortment of quality improvements.

With these Structure Tags, users can now sort and filter data in the Data Directory, delivering effective data organization capabilities on the platform. But while we realize getting your data into the platform is important – getting insights from your data off the platform is even more important. There are already plenty of options available to do this. You can get these insights from the user interface (UI), exporting your data via CSV files and our APIs. You can now use SQL or your own preferred tools to obtain deep insights into your data.

So, for the rest of this blog post, I want to give you a deep dive into a new, valuable tool available for Risk Modeler users on the Risk Intelligence platform that will help you generate reports – regardless of how you access your data or deliver your final report output.

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Spread the Word on Building Resilience for Vulnerable Communities

It was off to another prestigious London venue last week for the RMS team, to attend the Insurance Post British Insurance Awards at the Royal Albert Hall. In addition to fulfilling lifelong dreams to see Rick Astley perform live, the RMS team was also competing for the Risk and Resilience Award, alongside four other very worthy contenders. And, first presentation of the night, I was delighted to represent RMS to collect this important award.

This award recognized our longstanding charity partner Build Change, who we have worked together with for six years. Both organizations share a mission: to reduce lives lost from disasters by strengthening the built environment in economically deprived areas.

By combining RMS’ risk modeling expertise and institutional support with Build Change’s technical knowledge and grassroots approach, we’ve been able to demonstrate that retrofitting buildings, from homes to schools, in vulnerable neighborhoods across the globe can significantly reduce economic loss and save lives. And one of our many collaborations was an initiative to greatly improve the safety of seismically-vulnerable communities in Colombia.

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How Did the 2017-2018 Hurricanes Affect Medium-Term Rates?

Earlier this year, RMS released its latest medium-term rates (MTR) forecast for the North Atlantic hurricane basin as part of the North Atlantic Hurricane Models Version 18.1 release. Applicable over the 2019-2023 period, the Version 18.1 forecast represents an update from the previous MTR forecast issued in 2017 for the 2017-2021 period, by reflecting hurricane activity from the 2017 and 2018 seasons.

The MTR forecast provides a forward-looking estimate of the expected average annual landfall rate on a five-year horizon. Available alongside the long-term rates (LTRs) – a view of hurricane frequency based on the climatological average for the period from 1900 onwards, MTRs provide an additional perspective on expected hurricane rates on a shorter timescale. This allows RMS to adjust our view of risk according to the observed climate variability, and to combine different scientific theories on the drivers of hurricane variability over time, ultimately providing a view of landfalling hurricane risk that best represents the near-term basin conditions.

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RMS Location Intelligence API: Underwriting with 20/20 Vision

This is a taster of an article published in the latest edition of EXPOSURE magazine featuring the RMS Location Intelligence API. For the full article click here or visit the EXPOSURE website.

The insurance industry boasts some of the most sophisticated modeling capabilities in the world. And yet the average property underwriter does not have access to the kind of predictive tools that carriers use at a portfolio level to manage risk aggregation, streamline reinsurance buying and optimize capitalization.

Detailed probabilistic models are employed on large and complex corporate and industrial portfolios. But underwriters of high-volume business are usually left to rate risks with only a partial view of the risk characteristics at individual locations, and without the help of models and other tools.

Many insurers invest in modeling post-bind in order to understand risk aggregation in their portfolios, but Ross Franklin, senior director of data product management at RMS, suggests this is too late. “From an underwriting standpoint, that’s after the horse has bolted — that insight is needed upfront when you are deciding whether to write and at what price.”

By not seeing the full picture, he explains, underwriters are often making decisions with a completely different view of risk from the portfolio managers in their own company. “Right now, there is a disconnect in the analytics used when risks are being underwritten and those used downstream as these same risks move through to the portfolio.”

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