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:
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Keep updated with the latest information and insights from Moody's RMS Event Response and our wildfire experts.
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.
Against a backdrop of escalating natural disasters and increasingly interconnected risks, the 2023 catastrophe review report reveals a year marked by the rise of secondary perils.
Moody’s comprehensive modeling solutions capture risk correlations across primary and secondary perils.
Schrödinger’s cat concept applies to risk modeling, like flood defenses or fire risks, where outcomes are binary and unpredictable. Modelers must consider all scenarios for accurate risk assessments and effective disaster preparedness.
London’s Grenfell Tower fire highlights the catastrophic risk of flammable cladding on high-rises, underscoring the need for immediate action and reassessment of fire safety protocols and materials used in building exteriors.
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 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.
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