The Moody’s RMS™ HD Models are the latest generation of our probabilistic modeling suite. Available via our cloud-native applications — Risk Modeler™ and UnderwriteIQ™ — these models are designed to provide the most realistic representation of losses for both detailed and aggregate exposures, helping users improve capital allocation, property underwriting, and portfolio performance.
Our models provide a major step forward in the quality of catastrophe risk quantification, delivering deep insights that can inform risk management practices and business decision-making. We at Moody’s tailored our HD modeling framework for cloud-native architecture, leveraging enhanced processing capabilities for model calculations and outputs. This approach helps modelers to obtain deeper insights into their risk assessments.
By delivering deeper visibility into key risk drivers and greater control over model assumptions, HD models reduce model miss and support more confident decision-making. From rate making to capital planning, these models are designed to improve the accuracy and impact of your most critical business strategies. It’s not just about better models — it’s about better outcomes.
The HD framework underlying all of Moody’s RMS HD Models is designed to take advantage of today’s exceptional computing power and scientific and data analytics advances to offer exceptional clarity, precision, and adaptability, helping (re)insurance industry leaders make informed decisions that drive strategic growth and mitigate risks effectively.
Our HD models represent hazard event frequency by using temporal simulation analyzed across one-to-six-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.
Our HD models define the damaging features of the event in high resolution (up to a one-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 earthquakes, ground slope for floods, and vegetation density for wildfires.
Our HD models use four 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.
Our HD models use a period loss table, which represents losses for each sampled event. Express cedant terms and conditions in how reinsurance contracts, such as reinstatements and aggregate covers, are structured today to better quantify the impact of temporal and aggregate contract features.
We help insurers manage catastrophe risks, from earthquakes and hurricanes to floods and wildfires, with comprehensive solutions.
Moody’s solutions help exposure managers identify loss drivers and vulnerabilities across portfolios efficiently from a single source of truth.
We enhance underwriting with advanced science, data, and technology, enabling confident pricing, streamlined workflows, and unified analytics.
Flood defenses play a vital role in protecting people and properties against flooding in the UK. The frequency and severity of floods is expected to increase over the coming decades, which will reduce the effective protection of the UK’s flood defenses.
The growing impact of secondary perils on losses is leading to increased pressure on year-over-year financial performance. This whitepaper unravels the complexities of understanding large-scale catastrophes’ impact and how smaller yet frequent events can erode yearly earnings.
Floods are a major driver of global insured catastrophe losses, with losses growing over the last decade. Our eBook explores how Moody’s RMS HD Models can help you confidently manage flood risk.
Model convergence, which occurs when the distribution of simulated losses stops changing significantly with additional simulations, is an essential concept that informs how we manage modeling uncertainty and its downstream impact on a wide range of workflows, from underwriting to capital management.
Tropical and extratropical cyclones account for the majority of the world's costliest natural disasters since 1900. Our eBook reveals five ways Moody’s RMS HD Models help you manage windstorm risk with confidence.
Southern California Edison updated its public safety power shutoff (PSPS) program and wildfire mitigations, significantly reducing risks and impacts from PSPS. Moody's RMS model validated these efforts, aiding in future strategic planning.
A European insurer enhanced flood risk analytics with Moody’s RMS, improving pricing, risk selection, and portfolio management as well as achieving efficient model integration and reduced total cost of ownership via cloud-based solutions.
Low-resolution exposure data refers to when an individual location or portfolio has limited site details for modeling. For example, locations may only include ZIP or CRESTA-zone information that covers very large geographic areas. It also means the model will have significant impacts on modeled loss, particularly for highly granular perils like flood, where even a few feet difference can dramatically change the hazard level.
Moody’s RMS HD Models can help you overcome exposure data challenges with two approaches: aggregate loss profiles and our 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 methods. This process allows users to 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 aggregate loss model (ALM) profiles benefit from over 30 years of risk experience to deliver insights in minutes or even seconds. ALM leverages our extensive industry exposure databases and supplements this data with assumptions regarding geographic distributions, construction inventories, and insurance policy structures.
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 portfolio with over 1 million locations, 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 work to prepare the data.
To meet risk analysts’ and cat modelers’ complex needs at scale, Moody’s 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 (over 1 million locations) against Moody’s RMS HD Models, the industry’s most detailed and complete probabilistic models. All RiskLink DLMs and ALMs, as well as HD Models, can be run through Risk Modeler, providing users the power and speed that cloud-native architecture allows. As an example, running 1 million locations against the North Atlantic Hurricane DLM historical event set was 36 times faster in Risk Modeler.
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 that 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 Intelligent Risk Platform utilize a shared financial engine, applying (re)insurance policy terms 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 clauses, reinstatements, and multi-year contracts.
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