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Risk analytics and models in the cloud present immense business opportunities.
The Moody’s RMS® HD models are the latest generation of our probabilistic modeling suite. With the cloud-native modeling application, Risk Modeler, HD models offer more robust catastrophe risk modeling and are designed to provide the most realistic representation of losses for both detailed and aggregate exposures.
Unlock new levels of model transparency to make strategic decisions with confidence.
Harness wider model scopes to reduce unmodeled risk, better reflecting actual earning risk.
Adapt modeling parameters to better reflect to your corporate view of risk.
It is expected that providing wider insights into risk analysis and decision making against recent changes in natural catastrophe will be available by working with Moody’s RMS models.
With the increase of frequency and severity of wildfires in California, Southern California Edison (SCE), a retail utility company that handles electrical distribution and transmission, is investing more in mitigating wildfire risk while trying to reduce the impact of shutdowns on customers.
In partnership with SCE, Moody’s RMS utilized our North America Wildfire HD Model to assess where potential losses were most likely to be triggered and measure the impact of wildfire mitigation.
The basic framework for catastrophe modeling can be broken down into the following four modules. For each module, the HD framework offers unique capabilities that enable you to achieve a better understanding of risk to improve decision-making.
Hazard Module
Vulnerability Module
Financial Module
HD models define the damaging features of the event in high resolution (up to 1 meter grid) as well as any site conditions that could influence the impact of the event - crucial for high hazard gradient perils like flood and wildfire. This could include site characteristics like soil composition for earthquake, ground slope for flood, and vegetation density for wildfire.
HD models utilize 4 parameters to define vulnerability curves, accounting for the probability of 100% loss and zero loss. This innovative approach provides more realistic location-level losses and improved claim severity and frequency distributions.
HD models utilize a period loss table, which represents losses for each sampled event. Express cedant terms and conditions in how reinsurance contracts are structured today, such as reinstatements and aggregate covers to better quantify the impact of temporal and aggregate contract features.
The HD modeling framework extends the boundary of traditional catastrophe models and the use cases they serve. Let’s look at some of the ways in which HD Models can help you improve your most critical insurance workflows.
Low-resolution exposure data refers to when an individual location or portfolio has limited site detail for modeling. For example, locations may only include ZIP or CRESTA-zone information that cover very large geographic areas. It also ensures that the model will have significant impacts on modelled loss, particularly for highly granular perils like flood, where even a few feet difference can dramatically change the hazard level.
Two factors that can impact model run-times are the number of portfolio locations and the size of the event set for a given peril/region model. A highly granular peril model, such as flood, will often have significantly larger event sets and when coupled with a >1 million location portfolio it can take several days to run. Analysts often use workarounds to reduce run-times including splitting larger portfolios into smaller sections and then aggregating the results, but this can create a significant amount of time-consuming manual extra work to prepare the data.
Insurers often use multiple solutions to analyze the same portfolio including different modeling software, model versions, vendors and exposure management tools. Each tool typically uses a distinct financial engine which incorporates different methodologies for capturing and applying insurance and reinsurance policy terms leading to inconsistencies when generating and aggregating losses.
Windstorms remain the main driver of natural catastrophe insured loss in Europe, due in part to high levels of insurance penetration across the region. While individual claims might be small, the sheer size of these storms means multiple countries at a time are often impacted, resulting in significant cumulative costs to (re)insurers. Regulatory focus is therefore high, with (re)insurers required to provide evidence that they hold adequate capital for these types of events. The complex dynamics of extratr...
The RMS high-definition (HD) models are the latest generation of the RMS probabilistic modeling suite, incorporating features that allow flexible catastrophe risk management to meet the requirements of the evolving catastrophe risk market. But what does HD mean for the users of the models, and their metrics and analytics? Innovation driving quality of modeling insights RMS has been building catastrophe risk models for more than 30 years. The RiskLink® DLM models you use today, the first models built to acc...
As a catastrophe risk modeler, RMS® provides a range of modeling options that best accommodate our clients' needs. One focus area for clients is around the availability and quality of their exposure data, which can necessitate using limited aggregated exposure data to manage their risks. There are a host of reasons why a (re)insurer might not use detailed exposure data for modeling. In developing markets, detailed exposure data may not exist or not be reliable. A (re)insurer could be entering a new mar...
In February 2022, a sequence of powerful windstorms known as Dudley, Eunice, and Franklin (also known as Ylenia, Zeynep, and Antonia respectively) caused billions of euros of insured loss in Europe. With the first anniversary of these storms, we are reminded of the need to consider clustering when modeling windstorms in Europe. The 2022 windstorms happened as a large temperature gradient over the North Atlantic led to the formation of a series of low-pressure systems, which were then associated with a strong ...
At the RMS Exceedance 2020 virtual conference in May, we shared with attendees our product release plan covering a number of models. Today, we are delighted to announce the availability of three new models for our customers. Together with the U.S. Wildfire HD Model, these models all use the new high-definition (HD) modeling framework and are available in our Risk Modeler 2.0 application today. These include: Japan Typhoon and Inland Flood HD Model: Latest view of risk, incorporating learnings fr...
On November 13, 1972, now some 50 years ago, one of the strongest and most devastating windstorms struck Europe. Back in 1972, I was a teenager living with my family in northern Germany, and for us, this Autumn day started like any other. My siblings and I took the train to school which was some 15 kilometers (9 miles) from where we lived. At that point, we had no idea that this day would become one that we would remember for the rest of our lives. Quimburga, or the Lower Saxony Storm, originated on Nove...
Risk analytics and models in the cloud present immense business opportunities. The new version of the RiskModeler application, part of the Risk Intelligence platform from RMS, provides a unified model execution platform for core RiskLink models and our new HD models. This new version of RiskModeler also simplifies your workflows and greatly improves your total-cost-of-ownership. In this month’s webinar, we will focus on the Top 5 Reasons to Adopt HD Models and cover the following topics: The HD model su...
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