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Putting the Power of ​HD Modeling to Work for Our Customers

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. 

Reduce Model Uncertainty

Deliver a more complete view of losses by leveraging our industry-leading stochastic event sets

Improve Capital Management

Better understand the impact of risk correlations between events and perils in the tail

Overcome Exposure Data Gaps

Confidently model aggregate data with our advanced exposure disaggregation methodology


Wildfire Risk: Quantifying the Impact of Mitigation Measures in the Power Sector

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.

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High-Definition Catastrophe Modeling Framework​

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. 

HD Models

Stochastic Module

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Hazard Module

Vulnerability Module

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Financial Module

Temporal Simulation of Stochastic Event Set

Moody’s RMS HD models represent hazard event frequency by using temporal simulation analyzed across 1- to 6-year periods. A temporal simulation framework makes it possible to model time dependencies such as seasonality, event clustering, and antecedent conditions, while still generating familiar average annual loss (AAL) and exceedance probability metrics.

High-resolution Hazard Data Layers

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. 

Four-Parameter Vulnerability

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.

Period-Based Losses

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.

Why Invest in HD Modeling?

There are a lot of reasons to switch to the HD modeling framework. Let’s dive into a few of the reasons our customers are upgrading their models to HD or adding HD models to their modeling portfolio for high gradient perils like flood and wildfire.

Advanced Temporal Simulation

Model the time-dependencies of events including seasonality, clustering, and antecedent conditions for a better understanding of tail risk.

Comprehensive Event Sets

Utilize the industry’s largest stochastic event sets coupled with high-resolution hazard to effectively manage and understand cross-country correlation.

Flexible and Transparent Financial Modeling

Sample damage ratios for each location at the coverage level, per event, for the accurate calculation of contributory and marginal risk metrics.

Innovative Exposure Disaggregation

Capture a more realistic representation of a locations position, its characteristics, and the impact of localized hazard severity for greater accuracy of modeled losses for low-resolution exposure.

Moody's RMS High-Definition Model Portfolio

Frequently Asked Questions

Will I Still Benefit from a Robust Catastrophe Modeling Framework if I Have Only Low-Resolution Exposure Data?

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.

Moody’s RMS HD catastrophe models can help you overcome exposure data challenges with two approaches: Aggregate Loss profiles and its new disaggregation methodology.  The disaggregation methodology distributes low-resolution exposure data to high-resolution based on data layers including land-use and new Moody’s RMS methods.  This process ensures users can still benefit from the full suite of HD model innovations.

For those who want to run the latest, award-winning Moody’s RMS HD models in low exposure areas, our ALM profiles benefit from 30+ years of risk experience to deliver insights in minutes or even seconds.  ALM leverages our robust IED, and supplements this data with assumptions regarding geographic distributions, construction inventories, and insurance policy structures information available.

How Can HD Modeling Increase My Responsiveness to the Business? Often Running a Large Portfolio Can Take Weeks to Complete.

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.  

To meet the complex needs of risk analysts and cat modelers at scale, Moody's RMS developed Risk Modeler, a next-generation cloud-based modeling application on the Intelligent Risk Platform. As a cloud-native solution, Risk Modeler has the power to quickly run large portfolios (>1 million locations) against Moody's RMS HD Models, the industry’s most detailed and complete probabilistic models. All RiskLink DLM and ALM, and HD Models can be run through Risk Modeler ensuring that every analyst benefits from the power and speed that cloud-native allows. As an example, running 1 million locations against the North Atlantic Hurricane DLM historical event set was 36x faster in Risk Modeler.

How Do I Ensure That My Financial Modeling Is Consistent across Models, Perils and Applications?

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.

The applications on the Moody’s RMS Intelligent Risk Platform utilize a shared financial engine, ensuring (re)insurance policy terms are applied consistently at every stage of the portfolio analysis process.  The advanced financial model has been designed to capture the most complex policy terms including hours clause, reinstatements, and multi-year contracts. 

Learn More About HD Models

Learn More About HD Models

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