If there is anything the past decade has shown us, it’s that climate issues can no longer be relegated to the back burner. The frequency, volatility and severity of catastrophic events are inching upward as communities struggle to predict and prevent repeat disasters.

For insurers, the past year especially has brought sudden operational and economic changes and an urgent need to efficiently address how risks are detected and monitored. The question is, how can it be done?

The underwriter’s dilemma

While climate change is not a new concept for insurers, climate change data has yet to be effectively integrated into risk assessment models. This has left underwriters in a particularly difficult situation as climate disasters become more frequent and seemingly random each year.

Australia is a perfect example of such unpredictability. Over a billion animals were killed in the 2019-2020 Australian bushfires, and over $4.4 billion in damage was dealt to locals, according to McKinsey & Co. Market failures are another misfortune P&C companies will need to look out for as climate issues overwhelm entire continents. The global economy must be kept on its toes if it’s to avoid devastating losses over the next several decades.

Insurers must be careful not to overlook the implications of these themes and assume that policyholders can afford ever-rising rates — and that they can afford to insure climate-related catastrophes. Natural disasters like wildfires, tornados, floods, and hurricanes will be increasingly difficult to insure unless underwriters are provided with the tools they need to make more confident risk assessments.

Thankfully, that process is already underway.

Learning to decipher the unknown

Many natural factors impact both temporary and long-term variations in climate patterns — this we know. But there is also a significant amount of cause and effect that is still unknown as the planet continues to heat up. Scientists are still struggling to fully understand the patterns behind destructive events like tropical cyclones, especially in the midst of sudden temperature and pressure changes. At this unprecedented juncture in the earth’s history, experts are still unsure of what this will mean for different regions and the types of properties that exist there.

While typically viewed as less costly and destructive to communities, secondary perils are making news at an increasing rate. Widespread storms in Asia and blazing fires in Australia in 2019 showed how initial disasters quickly set off a cascade of subsequent hazards like floods and scorching temperatures.

According to the Swiss Re Institute: “Many climate change studies have suggested an increase in risks of between 20-to-50% over a 50-to-100-year period — a hypothesis that remains highly uncertain. Even an annual change averaging 1% or less can be misleading as the actual volatility of risk exposure can be much higher.”

With that being said, there are plenty of methods insurers can use to know more about the effects of climate change. Historical data has shown to be highly effective in helping insurers anticipate disasters. This reliable first-party data is helping insurers develop stronger catastrophe risk models and identify potential problems. As models are designed, components are being analyzed alongside historical data to determine weak points in a property. This also allows insurers to make quick decisions and underwriting changes to property contracts in the wake of a calamity.

Property AI for insurers & underwriters

With the rising levels of peril, besides adopting adequate pricing, reinsurers will need to adapt insurance solutions across the value chain to limit unsustainable losses. Artificial intelligence (AI) analytics track the characteristics of properties using multi-source imagery, thus reducing the need for inspections and raising return-on-investment. While older industries may be slow to consider AI as a viable solution to their problems, now is the time for P&C companies to get ahead of the curve. In the wake of COVID-19, digital-first companies have circumvented problems while their competitors struggled. Unable to perform in-person property inspections and handle in-person paperwork, AI allows these P&C professionals to transition to smoothly remote, digital work.

John-Isaac “jC” Clark, CEO of Arturo, has a deep background in geospatial and location-based services employing machine learning and big data analytics in both startup and corporate environments.

Prior to joining Arturo, he served as head of commercial product at MAXAR (formerly DigitalGlobe), where he worked to unify products across imagery, derived data sets, and machine learning-powered analytics. Clark was also a co-founder at T-Sciences where he spent nearly a decade building location-based analytic products in close concert with Google Earth and Google Maps and developed a passion for all things location.