Earnings perils: Redefining the risks that matter

For good reason, the insurance industry has primarily focused on what it regards as ‘primary’ perils – namely, tropical cyclones and earthquakes – as catastrophic events like Hurricanes Andrew, Katrina, Ian, and the Tōhoku earthquake, have generated significant market losses –and even threatened an insurer’s survival. If you focus on Europe, for instance, windstorms would be regarded by the industry as a primary peril and have the potential to generate a multi-billion euro-insured loss.

But for Europe, the cumulative effect of small to mid-sized loss events, such as flooding, hailstorms, or tornadoes – so-called ‘secondary’ peril events – can lead to increasing and alarming levels of loss for many (re)insurers. Single secondary peril events can be multi-billion-euro events, such as floods in Germany in 2021, which saw insured losses range between €5bn to €6.5bn.

Flood events in northern Italy earlier in 2023, or hailstorms in France in 2022, have all reached the billion-euro level. And according to a Gallagher Re report, during 2022 “… secondary perils were again the most expensive on an economic basis and exceeded those on the insured loss side.”

The frequency of secondary peril events in Europe will more than regularly outpace that of primary peril events, and severe convective storms and floods are often more unpredictable and localised than a pan-Europe windstorm. Secondary perils are also more prone to being exacerbated by both climate change and external economic factors such as increases in property exposure and inflation.

Erosion of earnings

A severe European windstorm can indeed generate industry-wide losses impacting large swathes of the market, with all insurers taking their share of the losses. But for secondary perils, any uptick in severe weather events and an increased volume and magnitude of claims for a specific insurer, can see secondary peril losses chewing away at a firm’s earnings, with c-suite executives then asking why earnings performance lags their peers.

Small, frequent secondary peril events can cause a year-over-year erosion of earnings, contributing to earnings risk, which is inherently tied to loss volatility. As these are smaller events, the individual loss events slip under the insurance firm’s risk radar, viewed as too small to take seriously with dedicated modelling, or just too sporadic and localised to be understood.

But if these smaller secondary peril events are dominating losses and challenging the status of primary events, is it time to drop this ‘secondary’ label and reflect the true scope of their potential impact – as earnings perils?

There is an understandable logic around investing in cat risk modelling for a primary peril that in a single event can cause a big loss, and a loss that could threaten liquidity and capital availability. But if cumulatively, secondary peril losses are challenging total losses from primary perils, then these perils must justify the same level of sophisticated modelling as is expended on primary perils.

However, measuring earnings perils can pose a challenge, as it requires the use of risk models with a high level of detail, the ability to aggregate and measure correlation across multiple perils within the same event, and the capability to financially model complex policy terms and outwards reinsurance policies.

But growing computing power through the cloud, together with technological advances over recent years, is helping deliver the required level of granularity to more accurately model high-gradient perils.

Moody’s RMS recently published a white paper using European climate risk as a case study to examine the market challenge of understanding earnings risk, with three key findings. You can download the white paper here.

High-definition risk modelling

Over the last few years, Moody’s RMS has built its high-definition risk modelling framework used in our risk modelling across many country markets and perils.

This framework embodies new approaches, such as temporal modelling and event simulation across long-time scales of 50,000 years, to account for tail risk and deliver high-resolution location-specific analysis.

High-definition (HD) models have been introduced in Europe for all main climate perils. Unique in the market, Moody’s RMS suite of European high-definition models covers windstorms, severe convective storms and floods.

Whether gaining insights on a single peril for a single country, multiple countries and perils, or a pan-European view of risk, these capabilities provide underwriters with a more informed understanding of the frequency and severity of modelled perils and a more realistic representation of risk.

It is worth noting that the decision to classify a peril as an earnings peril or a primary peril will depend on a (re)insurer’s portfolio, given that a (re)insurer’s portfolio might have limited windstorm exposure but significant flood exposure.

Regardless, introducing the term ‘earnings perils’ underscores the significance of these risks and their potential impact on the profitability of a (re)insurer.