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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. 

Improve Capital Allocation

Unlock new levels of model transparency to make strategic decisions with confidence.

Elevate Portfolio Performance

Harness wider model scopes to reduce unmodeled risk, better reflecting actual earning risk.

Reduce Model Uncertainty

Adapt modeling parameters to better reflect to your corporate view of risk.

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Mastering Earnings Risk: A Study on Europe Climate Portfolio Management

In the competitive realm of Property and Casualty (P&C) insurance, understanding earnings risk is becoming increasingly important. This white paper bridges the gap between C-suite executives and catastrophe modeling teams, unraveling the complexities of understanding not only the impact of large-scale catastrophes, but also how smaller, more frequent events can erode yearly earnings.

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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.

A New Way to Model Risk

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.

Capital Management

Accurate cross-country and cross-peril correlation enables you to make informed decisions on how best to deploy capital.

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Reinsurance Placement

Up to 5x more extreme events generates the most accurate view of tail risk, empowering you to optimize your risk transfer decisions.

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Claims Management

Innovative vulnerability module realistically models loss severity and frequency distributions to accelerate deployment of claims adjusters.

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Risk Pricing

Advanced and flexible financial engine captures bespoke hours clause policy conditions, aggregate treaty terms and multi-year contracts.

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Event Response

Comprehensive stochastic event sets of up to 8 million events deliver richer event selection during a live event, for a more accurate understanding of potential losses.

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Underwriting

Highly granular insights across perils and geographies help you to accurately identify drivers of loss and tailor your portfolio to your underwriting philosophy.

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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. 

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May 6-9 | Fairmont The Queen Elizabeth | Montréal, Canada
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