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Insurance Solutions

Formerly Moody’s RMS

Catastrophe modeling allows insurers and reinsurers, financial institutions, corporations, and public agencies to evaluate and manage natural and man-made catastrophe risk from perils ranging from earthquakes and hurricanes to floods and wildfires.

Catastrophe modeling framework

The basic framework for modeling the impacts of natural hazards on building inventories can be broken down into the following four modules:

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Event module


Hazard module


Vulnerability module

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Financial module

Stochastic event module

The first stage of catastrophe modeling begins with the generation of a stochastic event set, which is a database of scenario events. Each event is defined by a specific strength or size, location or path, and probability of occurring or event rate. Thousands of possible event scenarios are simulated based on realistic parameters and historical data to probabilistically model what could happen over time.

Hazard event module

The hazard module assesses the level of physical hazard across a geographical area at risk. For example, an earthquake model estimates the level of ground motion across the region for each earthquake in the event set, considering the propagation of seismic energy. For hurricanes, a model calculates the strength of the winds around a storm, considering the region’s terrain and built environment.

Vulnerability module

The vulnerability module assesses the degree to which structures, their contents, and other insured properties are likely to be damaged by the hazard. Because of the inherent uncertainty in how buildings respond to hazards, damage is described as an average. The vulnerability module offers unique damage curves for different areas, accounting for local architectural styles and building codes.

Financial module

The financial module translates the expected physical damage into monetary loss. It takes the damage to a building and its contents and estimates who is responsible for paying. The results of that determination are then interpreted by the model user and applied to business decisions.

Superior catastrophe models

Uncertainty is constant and catastrophe is unpredictable.  As natural catastrophes continue to increase in intensity and frequency, the ability to understand and quantify the potential impact of events, including their tail risks and influence on earnings, is now more important than ever.

The Insurance Solutions team at Moody's is dedicated to providing our customers with superior catastrophe modeling that integrates innovative analytics, technology, and science. Learn more about what makes us different.

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Defining the industry’s currency of risk


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Solutions driving real world success

See how customers use our catastrophe models to outperform.

A data-driven, insight-led approach to pricing and underwriting

Covenant Underwriters saw a disconnect between how it priced business and how partner carriers viewed the same risks when assessed with probabilistic catastrophe models. Their portfolio was also impacted by claims arising from hurricanes and severe convective storm events. Overall, there was a need for access to location-based catastrophe risk modeling insights at the point of underwriting to enable a more sophisticated and consistent approach to rating.

Covenant Underwriters

Leading non-life insurer raises the data level for flood insurance

A leading European regional non-life insurance company wanted to grow its position in the regional flood insurance market. The company recognized that to achieve this growth it needed to enhance its existing analytical and catastrophe modeling. By integrating Moody's RMS Flood HD Models into their workflow, the company was able to boost its portfolio and achieve an on-demand view of flood risk.

Flood Insurer

Evaluating the performance of UK flood defences under climate change

Flood defences play a vital role in protecting people and properties against flooding in the U.K. As climate change increases flood risk over the coming decades, the frequency and severity of floods will increase. This will reduce the effective protection of the U.K.’s flood defences, as the likelihood of flooding overtopping defences increases.

flooded town

Wildfire risk: Quantifying the impact of mitigation measures in the power sector

Southern California Edison (SCE) is a retail utility company that handles electrical distribution and transmission serving 15 million people across a region of 50,000+ square miles.


A mature approach to exposure and catastrophe risk management

IRB Brasil Re has a long history of reinsurance market leadership in Latin America and continues to maintain that position in nine out of its eleven business lines while also growing to be among the ten largest reinsurers in the world in market value (BRL 31 billion as of May 2019).

Flooded houses in Houston

The emerging threat: Earning perils

As the risk landscape continues to change, ‘secondary’ perils are moving into the spotlight. Over the last decade, the insurance industry has witnessed a significant increase in losses from secondary perils which have also been exacerbated by factors such as exposure spread, climate change, and economic development.  ​

While primary perils typically produce industry-wide losses impacting large swathes of the market, it is secondary peril losses impacting selected (re)insurers that result in the C-suite asking why earnings or financial performance lag behind their peers. ​

So, perhaps now is the time to redefine ‘secondary perils’ as ‘earnings perils.’ 

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Pillars of high-quality Catastrophe Models

Curiosity, passion, and innovation make high-quality models.

Having the best models is key to enabling the (re)insurance industry to make crucial decisions. Catastrophe models inform insurers how to manage risk aggregations, deploy capital, and price insurance coverage. It also allows insurers to demonstrate capital adequacy to rating agencies.


We continue to add higher resolution data and ensure it is accurate and up to date because model results are only as robust as the quality of the data entered into them.


We build comprehensive solutions and calibrate our models using the most recent events by collaborating with clients, institutions, and the scientific community.


Our people bring decades of science, technology, and industry expertise to stay in the forefront of catastrophe modeling and help customers implement risk modeling best practices.


We leverage the power and capacity enabled by Cloud-computing environments and latest technologies to conduct intensive data analysis using robust analytics.

Understanding Catastrophe Models (FAQs)

Just because a catastrophic event hasn’t occurred in the past doesn’t mean it can’t or won’t. A combination of science, technology, engineering knowledge, and statistical data is used to simulate the impacts of natural and man-made perils in terms of damage and loss.

How can Catastrophe Models be used today?

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 savvy surveyors include catastrophe modeling data in surveying reports.

How can you analyze the data that comes from a Catastrophe Model?

Catastrophe models help you understand risk by translating hypothetical natural or man-made peril losses into real-world impact on your portfolio. Modelers can understand catastrophe losses by running 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 = 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 their 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, despite the fact that 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. This is important because 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. 

What is earnings risk in the insurance industry? 

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.

How can catastrophe modeling help with managing earnings risk? 

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 then helps insurers to better price policies, set aside sufficient reserves, and purchase reinsurance coverage to mitigate their exposure, thereby maintaining financial stability and resilience.

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