Category Archives: Risk Modeling

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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Industry Collaboration for a Better Data Standard

Data – the buzzword of the decade. The world understands its value, but the insurance industry has not only lagged behind in exploiting data, it has also created huge inefficiencies in how it is handled and exchanged. At RMS, we believe that no single company is going to solve this problem – it will take collaboration.

The data that drives risk analytics has proven particularly tricky to handle and leverage. Right now, the only standards the industry uses are decades-old property cat schemas – venerable work horses that took the industry from an almost total lack of exposure data to a relatively high degree of understanding. They transformed how property cat risk is transacted, priced, and managed. But these old formats have run their course and if we want to gain meaningful efficiency, improve profitability, and pursue new opportunities beyond property cat, we need a new standard.

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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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The Future of Cyber Risk

What will cyber-risk look like in 2030? Given the rate of change of technology this may seem like an impossible question to answer. But for those making investments that depend on these new technologies and the risk that surround them – either managing or insuring the risk – it’s critical that these investments are being made not only with a 12 month horizon in mind, but with a projection that extends over the next five or even ten years.

To facilitate this important discussion, RMS is delighted to be co-hosting an event at the University of Cambridge Judge Business School on “The Future of Cyber Risk”. To be held on July 24, the event will challenge cyber risk specialists and risk managers to think beyond the next 12 months and to consider how cyber could evolve over a five- to ten-year horizon.

In particular, the event will focus on the potential paradigm shifts that could provide strategic shock, and how business strategies should be developed to cope with this uncertain future.

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London Bridge Terrorism Inquests: A Counterfactual Risk Perspective

The inquests into the deaths of eight individuals killed in a terrorist attack on London Bridge and in Borough Market on June 3, 2017, were held at the Central Criminal Court, Old Bailey, London, two years later – from May 7 to June 28, 2019. Judge Mark Lucraft, the Chief Coroner for England and Wales, concluded that the eight victims were unlawfully killed, but he was not convinced that MI5 and the police missed any opportunities that would have prevented the attack.

Three Islamist terrorists, Khuram Butt, Rachid Redouane, and Youssef Zaghba, rammed pedestrians on London Bridge, before launching a frenzied knife rampage around nearby Borough Market, a lively food market and tourist hotspot that attracts 4.5 million visitors per year. The three attackers were shot dead by police at 10.17 p.m., less than 10 minutes after the rampage began.

Although the Chief Coroner did not criticize MI5 and the police in his conclusions, he did criticize the lack of security barriers on the bridge, and concluded that this showed weaknesses in systems for assessing the need for such measures, and implementing them promptly. The attack happened only two-and-a-half months after the Westminster Bridge vehicle ramming attack on March 22, 2017.

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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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An Award-Winning U.S. Inland Flood Model

It was off to London’s Savoy Hotel for members of the RMS London team last Thursday, for the Eleventh Trading Risk Awards. And apart from the great hospitality, and the flowing conversation from colleagues and industry peers alike, RMS was also recognized by the award judges, receiving the “Initiative of the Year” award for the RMS U.S. Inland Flood HD model.

Without sounding like an Oscar acceptance speech, on behalf of the team that worked on the model, I would like to thank the judging panel made up of representatives from the media and the industry for selecting our entry. Released last October, the flood model is designed to help the private insurance market seize the opportunities presented by this peril, and to also ultimately help accelerate flood insurance take up in the U.S.

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The Value of Flood Protection: Quantifying the Benefits of Defenses Along U.K. Rivers

Whenever the U.K. is hit by major flooding, attention quickly turns to the performance of the nation’s flood defenses. Some defenses, such as London’s Thames Barrier, are regularly recognized for their vital role in protecting people and property. The value of other mitigation measures, however, has been frequently challenged, such as when defenses failed to prevent significant flooding in Cumbria during storm Desmond in 2015.

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SiteIQ and the Art of Empowerment

This is a taster of an article published in the latest edition of EXPOSURE magazine featuring the new RMS application – SiteIQTM. For the full article click here or visit the EXPOSURE website.

EXPOSURE magazine recently looked at the challenges that underwriters and agents at coverholders currently face, to get comprehensive risk data when evaluating an individual location.

When evaluating single risks, underwriters and coverholders typically have to request exposure analytics from their portfolio managers and brokers, or gather their own supplementary risk data from a range of external resources, whether it is from Catastrophe Risk Evaluation and Standardizing Target Accumulations (CRESTA) zones, through to lookups on Google Maps. But all this takes valuable time, requires multiple user licenses and can generate information that is inconsistent with the underlying modeling data at the portfolio level.

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