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Catastrophe modeling helps insurers, reinsurers, financial institutions, corporations, and public agencies evaluate and manage natural and man-made catastrophe risk from perils ranging from earthquakes and hurricanes to floods and wildfires. With over 400 models in nearly 100 countries, you can assess and estimate the likelihood and severity of future disasters and their impact on your portfolio.
Catastrophe modeling plays a crucial role in assessing and managing risk. With our advanced modeling solutions, organizations may price policies more effectively, set aside sufficient reserves, and purchase reinsurance coverage to reduce their exposure while maintaining financial stability and resilience.
The basic framework for modeling the impacts of natural hazards on building inventories can be broken down into the following four modules:
10 of the top 10 reinsurance brokers choose Moody’s
9 of the top 10 insurers of commercial lines choose Moody’s
28 of the top 30 reinsurers choose Moody’s
Catastrophe modeling allows (re)insurers, financial institutions, corporations, and public agencies to evaluate and manage natural catastrophe risk from perils ranging from earthquakes and hurricanes to flood and wildfire.
With news reports regularly tracking the current fast-developing, strengthening El Niño event, what does this all mean for insurers and reinsurers? In a new blog from Alyson Douglas, we go back to basics to outline what El Niño is, the current situation, regional impacts, what to expect in the coming months, and the importance of incorporating interconnected climate events like El Niño or La Niña into portfolio analytics, so the industry can understand how risk accumulates and what tools are available to transfer the risk.
From the East Coast floods in 2022, to Cyclone Alfred in 2025, wind and flood events have resulted in billions of dollars of insured losses for Australian insurers. With cyclone and flood underwritten in the same policies, and reinsured in the Cyclone Reinsurance Pool, Steffi Uhlemann-Elmer stresses the benefits of combining modeling for cyclone wind, cyclone storm-surge-driven coastal flood, cyclone-precipitation-driven inland flood and non-cyclone precipitation-driven inland flood in a new Moody's RMS model under a single high-definition (HD) framework, which explicitly accounts for the Cyclone Reinsurance Pool.
As this paper is being written in August 2026, major wildfires continue to burn across several parts of Europe, including France, Spain, Croatia, Belgium, and the United Kingdom, as well as in parts of the Americas.
US property catastrophe risk has entered a more volatile phase — one that is no longer defined by isolated peak peril events but by the cumulative impact of frequent losses across multiple perils.
With an announcement on the strategic migration of two flagship US risk models to the high-definition (HD) framework - the Moody's RMS North Atlantic Hurricane and US Earthquake models, Julie Serakos covers the key advancements in these models, and how these two new HD models add to existing wildfire, flood, and severe convective storm HD models to give a more unified view of risk, all running on the Moody's Intelligent Risk Platform.
To meet the growing need for faster, more precise risk insights, catastrophe models serve as the scientific backbone of risk assessment, with artificial intelligence (AI) emerging as a powerful accelerator to enhance their speed and impact. Firas Saleh provides three key areas where AI is making a transformative impact, from event response and the Los Angeles wildfires in 2025, enhancing model input data, to streamlining workflows. Firas gives practical examples of how cat models and AI are enabling a more resilient future, as stakeholders face mounting pressure to respond more rapidly and accurately in a changing risk landscape, to protect communities and maintain financial stability.
James Cosgrove summarizes Moody's RMS Event Response 2025 Catastrophe Review analyzing the main catastrophe events from last year, with insured losses dominated by wildfires in Los Angeles County, and severe convective storms in the U.S.
The protection gap — the difference between total economic losses and the share that insurance actually covers — is a widening, systemic risk. Here is why it matters, who absorbs the cost, and how catastrophe models can help bring it into view.
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Explore the key innovations that help the Moody’s RMS™ North America Severe Convective Storm (SCS) HD Models redefine the standard for SCS risk modeling, to deliver unprecedented accuracy and insight, all built on cutting-edge science and powered by Moody’s Intelligent Risk Platform.
High-definition models use advanced computing and data analytics for precise risk modeling, offering clarity and adaptability in insurance decision-making. They improve risk assessment accuracy through enhanced spatial coverage and granularity.
Moody’s comprehensive modeling solutions capture risk correlations across primary and secondary perils.
Uncover global risk insights with the world’s first open, modular, and unified risk platform and applications suite in the cloud.
Moody’s physical and transition risk solutions help organizations assess interconnected risks and help them make informed risk management decisions within their existing workflows.
Catastrophe models were originally developed to help the insurance industry underwrite rare but costly events. Today, industries beyond insurance are realizing the benefits of cat models. For example, catastrophe modeling allows land professionals to identify regions of potential risk and take proactive measures to mitigate their exposure. Government officials can use data from catastrophe models to set land-use policy in vulnerable regions. Lenders can also use catastrophe models to improve their lending practices, while surveyors include catastrophe modeling data in surveying reports.
Catastrophe models help you understand risk by translating hypothetical natural or man-made peril losses into real-world impacts on your portfolio. Modelers can understand catastrophe losses by analyzing a variety of loss metrics. Some of the key metrics include:
Exceedance probability (EP): EP is the probability that a loss will exceed a certain amount in a year. It is displayed as a curve to illustrate the probability of exceeding a range of losses, with the losses (often in millions) running along the X-axis and the exceedance probability running along the Y-axis.
Return period loss: Return periods are another way to express potential for loss and are the inverse of the exceedance probability, usually expressed in years (1% probability equals 100 years). While this can be thought of as the average rate of exceedance over the long term, it is more accurate to say, “This loss has a 1 in 100 chance of being exceeded this year.”
Annual average loss (AAL): AAL is the average loss of all modeled events or periods, weighted by the probability of their occurrence. In an EP curve, AAL corresponds to the area underneath the curve, or the average expected losses that do not exceed the norm. Because of this, the AAL of two EP curves can be compared visually. AAL is additive, so it can be calculated based on a single damage curve, a group of damage curves, or the entire event set for a sub-peril or peril. It also provides a useful, normalized metric for comparing the risks of two or more perils even though peril hazards are quantified using different metrics.
Coefficient of variation (CV): The CV measures the size, or degree of variation, of each set of damage outcomes. Damage estimates with high variation, and therefore a high CV, will be more volatile than an estimate with a low CV. Mathematically, the CV is the ratio of the standard deviation of the losses (or the “breadth” of variation in a set of possible damage outcomes) over the mean (or average) of the possible losses.
Earnings risk in the insurance industry refers to the potential variability in profits due to factors such as claims payouts, investment returns, and operational expenses.
Catastrophe modeling plays a crucial role in assessing and managing earnings risk, particularly for property and casualty insurers. By simulating the potential impact of catastrophic events on insurance portfolios, catastrophe models help insurers estimate the likelihood and severity of future disasters.
By integrating catastrophe modeling into risk management practices, insurers can better quantify and manage their earnings risk associated with catastrophic events. This helps insurers better price policies, set aside sufficient reserves, and purchase reinsurance coverage to mitigate their exposure, thereby maintaining financial stability and resilience.
Moody’s RMS catastrophe models cover a broad range of natural, specialty, and emerging risks across nearly 100 countries. Coverage includes:
Earthquake and tsunami: North America (including the United States and Canada), Latin America, Europe, Japan, New Zealand, and other seismic regions globally.
Hurricane, tropical cyclone, and typhoon: The United States, Caribbean, Latin America, Japan, Australia, New Zealand, and other cyclone-prone regions worldwide.
Flood: The United States, Europe, Australia, New Zealand, and other major inland and coastal flood zones globally.
Wildfire and bushfire: The United States, Canada, Australia, and other regions exposed to vegetation-driven fire risk.
Severe convective storm: The United States, Europe, and Australia, including risks from hail, tornadoes, and straight-line winds.
Winterstorm: North America, including the United States and Canada, as well as select European markets.
Terrorism: Major global markets and urban centers worldwide.
Cyber risk: Global coverage to help organizations assess and quantify cyber-related catastrophe risk.
Pandemic and infectious disease: Global modeling solutions that support the assessment of health-related systemic risk.
Agriculture: Key agricultural regions worldwide, helping insurers evaluate weather- and climate-related impacts on crops.
With more than 400 catastrophe risk models, Moody’s helps insurers, reinsurers, financial institutions, corporations, and public agencies assess exposures across both primary and secondary perils, supporting risk management, underwriting, portfolio optimization, and capital planning. Coverage varies by peril and region, with high-definition (HD) models available for many of the world's most significant catastrophe risks.
Interested in learning more about our offerings? Our solutions specialists are ready to help.
