Disrupting the flow of earnings risk losses

Maurizio Savina, senior director – product management, insurance solutions at Moody’s, on how modelling can help insurers get on top of earnings perils.

As an industry, we are all anxious to see how natural catastrophe activity will pan out in 2024. Are we set for another year of more than $100bn in annual insured losses or will 2024 be a below-average loss year?

Regardless of whether 2024 annual loss figures come in above, in line, or below expectations, we all know that risk carriers must carry the mandate from their investors and ensure they have long-term sustainable pricing models for both internal risk management and risk transfer to thrive in the good and bad times.

Much of the industry’s focus rightly remains on major nat cat events. These run into tens of billions of dollars in losses and have the power to upend communities, causing intense damage and loss of life and livelihoods. Severe hurricanes and earthquakes constantly loom, and insurers must be ready to provide significant financial lifelines for communities to rebuild and recover. One or two major nat cat events resulting in tens of billions of dollars of losses see the industry’s solvency and resilience tested.

But in terms of everyday insurance business, the less major nat cat events such as floods, severe convective storms (hail, thunderstorm, wind, tornado) and wildfires have escalated in frequency and severity. Multiple billion-dollar loss events can add up to the same level as a major nat cat loss, with the potential to generate sizeable insured losses as policyholders repair properties and look to return to normality.

There has been debate around losses from ‘primary’ or peak perils such as hurricanes and earthquakes, and those perils deemed ‘secondary’ or non-peak. We have argued that secondary perils should be reclassified as ‘earnings’ perils to reflect the impact of these more frequent, smaller nat cat events, namely that they eat directly into an insurer’s earnings.

If losses from earnings perils are greater than primary perils, the sustainability of insurers paying out for them from earnings should be questioned, especially by investors and other stakeholders.

They would be remiss not to ask how these earnings risk losses are managed, whether the risks are sufficiently priced, and whether risk selection is effective. And crucially, how could the insurer transfer excessive risk to the market?

It is a misconception that risks such as severe convective storms, wildfires and floods can’t be effectively modelled. Developments such as high-definition (HD) modelling, based on 50,000 years of pan-continent event simulation that incorporates the widest possible range of factors from climate to the presence of defences and mitigation measures, have brought a view of risk that is effective at the individual location level through to country and continent-wide portfolio levels, accounting for all relevant cross-country and cross-peril correlations.

Another misunderstanding is that HD modelling for earnings risk perils isn’t readily available, but this is rapidly countered by modelling providers such as Moody’s. Moody’s RMS HD Models cover all of the main earnings risk perils in Europe and Japan, and nearly all in the US and Australia, with more countries and regions to come.

We value the partnership with risk carriers and intermediaries that want to innovate with HD modelling and move to the most complete view of risk. For example, one response from insurers to rising earnings risk losses was to withdraw coverage from riskier areas, but HD modelling helps to examine the granular factors that can make one location insurable compared to a riskier location right next to it. This leads to a better understanding of risk to support pricing and selection decisions.

These HD modelling innovations are helping insurers get on top of earnings risk perils, rather than tolerate a steadily growing level of losses that chip away at earnings. Instead, they can take action and level up their earnings risk modelling to the same standard as for primary perils.