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Insurance Solutions

Formerly Moody’s RMS

When (re)insurers struggle to meet their cost of capital over a longer period, shareholders, board members, and other stakeholders may begin to question whether a firm understands the risk landscape within which it operates.

Questions can form around whether a (re)insurer not only understands the risks of large-scale catastrophes driven by established ‘peak’ perils such as tropical cyclones and earthquakes but also smaller and more frequent events typically from ‘non-peak’ perils that can eat into year-over-year earnings.

But where the management of peak peril risks – due to their significant loss potential, benefits from sophisticated risk modeling, loss events from perils such as severe convective storms tend to be smaller in terms of insured losses.

(Re)insurers tend to turn to their loss experience to establish the risk from non-peak perils or simply take the loss hit in their earnings.

Major peak peril loss events stand out and make global news; we know these events, single events from Hurricanes Andrew, Katrina, Ian, or the Tohoku earthquake, all resulting in tens or hundreds of billions of dollars in loss.

A recent Quarter Three 2023 cat report from Gallagher Re outlined that average annual global primary peril insured losses during the last decade were US$42 billion.

However, average annual non-peak peril insured losses during the same period were US$70 billion and had an annual growth of 6.9 percent above inflation since 2000.

Non-Peak Perils Risk Equals Earnings Perils

Whereas peak perils have the potential to directly impact solvency, and their impact often features as part of regulatory submissions, non-peak peril losses that erode earnings – better classed as ‘earnings perils’ – although lower per event, can be constant and pervasive.

Therefore, the management of earnings risk is crucial as it relates to the resilience of individual risk carriers and the overall reinsurance industry.

There are distinct challenges to understanding earnings risk perils, such as the very localized nature of loss events – a damaging hailstorm can cause concentrated losses.

The frequency and severity of non-peak events can quickly accumulate significant losses, though perils from floods, severe convective storms, or wildfires, are often unmodeled.

In the absence of catastrophe models, actuaries have played a major role in setting the cost of non-peak perils, mostly based on historical loss experience, though reliance on recent history has limitations in either omitting many potential extreme events or overemphasizing any extreme that falls within the sample period.

To examine these challenges with managing earnings risk, Moody’s RMS has published a white paper on European climate risk as a case study.

Challenges of Managing Earnings Risk

How are earnings defined? Earnings in the (re)insurance industry are traditionally calculated by subtracting expenses, interest, and taxes from revenues.

In our white paper, we focus specifically on revenue (the premiums collected on insurance policies) and expenses (the claims paid out), and not investment income or other more predictable expenses, such as IT and human capital expenses.

Investors start to become concerned about earnings risk when the volatility in losses exceeds expectations. Earnings risk can be measured in various ways, but for simplicity, in our white paper, we define it to be proportional to the aggregate exceedance probability 1 in 10 (AEP 1-10) of a firm’s book of business, normalized to the premium.

So, assuming companies have a good understanding of premiums, the question is: Why do companies underestimate the AEP 1–10?

For a typical (re)insurer, the AEP 1–10 at the portfolio level will include a broad range of perils, each with regional-level frequency and severity depending on the overall portfolio composition.

Depending on the region, and as seen in the Gallagher Re analysis, more than 50 percent of annual losses have been associated with secondary perils or non-peak perils.

To effectively manage the AEP 1–10 at the overall portfolio level, (re)insurers must have good control of a series of regional loss distributions at different frequencies and severity, with a tier ranking of importance based on the specific portfolio composition.

Traditionally, the industry considers European windstorms (wind and coastal floods) to be a Tier 1 peak peril, and it is certainly a peak peril for capital risk and solvency.

The most common practice at the time of a risk transfer is to price European climate risk based on European windstorms and, depending on the portfolio, also flood for Belgium, Germany, and the U.K. 

However, when isolating single-country contributions across all relevant perils (and sub-perils) and when focusing on earnings risk, it becomes clear that not all countries are equally important and other perils should also be considered Tier 1.

Some of the other main challenges with understanding the AEP 1–10 can be associated with a lack of cross-country and cross-peril correlation (the bad) and diversification (the good) that can see a re-insurer under-pricing coverages or overpricing its offering out of the market.

In addition, the idea that (re)insurers don’t need to understand the tail risk for a non-peak peril/region to manage earnings risk must be dismissed. This is a key question that many risk carriers have been trying to answer, especially reinsurers.

One major aspect to consider is that in recent years, catastrophe risk models have been extended to many new geographies and perils, helping to significantly reduce non-model components, benefiting both internal risk management and risk transfer practices, all of which can help build a more resilient market. 

I encourage you to download our white paper entitled “Mastering Earnings Risk” for insights and practical actions that the industry can take to start to understand and manage earnings risk.

Download the white paper here.

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Maurizio Savina
Maurizio Savina
Vice President of Climate Models - Product Management, Moody's RMS

Based in Zurich, Maurizio joined Moody's RMS in 2012 as an Account Associate and progressed to become Director of Model Product Management in 2018. He joined SCOR as Head of Catastrophe Risk Research and Development in 2019, before returning to Moody's RMS in 2022 as Vice President of Climate Models - Product Management, developing and managing Moody's RMS range of climate models.

Prior to Moody's RMS, Maurizio conducted postdoctoral research for the Chair of Hydrology and Water Resources Management at the Swiss Federal Institute of Technology Zurich (ETH Zurich). His main research interest was related to the improvement of our understanding of the hydrological processes driving mountain precipitation and flood hazards. He worked extensively with satellite and ground-based remote sensing as well as with mathematical modeling of precipitation and eco-hydrological processes.

Maurizio holds an MSc in Civil Engineering from the Polytechnic University of Turin and a PhD in Hydrology from the ETH Zurich.

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