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Attempting to make sense of rising global risks based on outdated catastrophe model approaches can feel like using a dial-up modem to surf the web in an age of fiber optics.
Scientific and technological advancements have changed the risk modeling landscape and have opened an ‘insights gulf’ that is increasingly hard to ignore, between risk model output that capitalizes on the latest advancements and output that does not.
Looking at my twenty-five years of leading catastrophe modeling and exposure management teams, in my experience, the insurance industry has primarily focused its attention on what is regarded as ‘primary' perils, namely tropical cyclones and earthquakes.
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.
Unlock new levels of model transparency to make strategic decisions with confidence.
Harness wider model scopes to reduce unmodeled risk, better reflecting actual earning risk.
Adapt modeling parameters to better reflect to your corporate view of risk.
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.
Moody’s RMS™️ has conducted work in partnership with Flood Re to examine how climate change could impact flood defence outcomes for York, England, and Pontypridd, Wales.
It is expected that providing wider insights into risk analysis and decision making against recent changes in natural catastrophe will be available by working with Moody’s RMS models.
With the increase of frequency and severity of wildfires in California, Southern California Edison (SCE), a retail utility company that handles electrical distribution and transmission, is investing more in mitigating wildfire risk while trying to reduce the impact of shutdowns on customers.
In partnership with SCE, Moody’s RMS utilized our North America Wildfire HD Model to assess where potential losses were most likely to be triggered and measure the impact of wildfire mitigation.
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.
Hazard Module
Vulnerability Module
Financial Module
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.
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.
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.
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.
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.
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.
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.
Situated at the meeting point of the Pacific and Indo-Australian tectonic plates, New Zealand is no stranger to seismic activity. Over the past 15 years, the country’s relationship with earthquakes and their insurance repercussions has taken a transformative journey, influenced largely by three pivotal developments: significant seismic events, market alterations, and leaps in scientific understanding. As the second-largest earthquake-insured loss worldwide, the 2010-11 Canterbury Earthquake Sequence (CES)...
At 11:58 JST on Saturday, September 1, 1923, an Mw7.9 earthquake struck the Tokyo, Japan region. The reported ground shaking lasted longer than four minutes and caused extensive damage to buildings and infrastructure. Total damage at the time of the event is estimated at 6.5 billion yen which is assessed by Moody’s RMS to increase to 48.5 trillion yen or $331B in economic loss in today’s dollars. While the damage from the strong ground shaking was severe and widespread, fires triggered by the earthquake swept...
Approximately a year after the U.S. terror attacks on September 11, 2001, Moody’s RMS released its first U.S. Terrorism Risk Model. Towards the end of 2002, the U.S. Congress passed the Terrorism Risk Insurance Act (TRIA) into law, which introduced the Terrorism Risk Insurance Program (TRIP) to provide a federal backstop for insurance claims related to acts of terrorism. The new Act required that U.S. insurers offer terrorism coverage for specific commercial lines of property and casualty insurance. T...
After severe drought conditions in Italy which can be traced back over the last two years, and the country recording its hottest year in 2022, severe rainfall events this May added yet another climatic extreme. Two separate severe rainfall events struck the Emilia-Romagna region (pop. ~4.4 million) in the north of Italy in just two weeks. Italy’s longest river, the River Po, defines the Emilia-Romagna region’s northern border, measuring some 290 kilometers (180 miles) from its northwest corner, located a...
The increasing frequency and intensity of floods in the U.S. over recent years have highlighted the need for holistic risk management solutions to inform decision-making and to price risk more accurately. Examining the more recent flood events from 2022 and into early 2023, homeowners and businesses from the Western U.S. through to the Eastern Seaboard found themselves grappling with the devastating consequences of widespread flooding. These events inflicted hardships on individuals, businesses, and thei...
A ‘recency’ bias focused on actual events over the past ten or twenty years, their locations, impacts, and consequences, is a predisposition that can apply to many catastrophe risk managers. However, we know through stochastic modeling that each actual event is just one sample from a wide range of possible events, which may all be equally likely. It is certainly helpful to contemplate whether recent catastrophes could have been more or less impactful through small variations in their track, source, or se...
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