Not long ago, insurers' principal interest in tracking climate-related and environmental, social, and governance (ESG) metrics was in satisfying compliance-related reporting requirements. Insurers relied on historical data from limited numbers of sources to do so.
While regulatory reporting remains a driver, that's changing fast as predictive analytics evolve from a compliance exercise to a strategic risk-management tool. Climate disasters will cost an estimated $328 billion this year, of which about 40% will be insured, and those numbers are expected to rise at about the same 6% annual clip as in the recent past.
So it's no surprise that 83% of insurance executives view predictive analytics as "very critical" for the future of underwriting. And it's cause for concern that just 27% of property and casualty (P&C) carriers say they have the ability to leverage predictive analytics in their underwriting models.
Predictive-analytics models can reduce risk exposure, identify insurable risks, and sharpen pricing. That combination can help boost profitability by avoiding losses and insuring what might otherwise have been avoided in a less-sophisticated era.
Investment-side benefits are at least as important as underwriting gains
The benefits of advanced analytics in assessing climate and risks related to environmental, social and governance (ESG) issues extend to insurers' investment portfolios. Without analytics touching investing as well as underwriting, insurers can find themselves exposed on both the claims and investment-portfolio fronts. For instance, as we enter hurricane season, an insurer with P&C liability as well as municipal bond holdings in coastal Florida could end up suffering a double hit after a storm sweeps through.
The question the roughly three-quarters of insurers who still lack climate and ESG-related analytics should be asking is not whether it makes sense to establish such capabilities but rather how to go about it. The playbook will differ depending on an insurer's scale, market distribution, and underwriting and investment portfolios. But there are three fundamental steps to consider.
First, predictive analytics is about data, and while generative AI may be able to work from the unstructured masses, predictive analytics and the emerging agentic AI that delves into the numbers need clean, high-quality data. In both cases, developing cloud-based repositories of rationalized data is essential. The data-analysis process typically leads back to applications, many of which can be trimmed down and consolidated – a bonus.
Second, predictive analytics needs tons of data, and from many sources. In the climate-risk realm, external weather and geospatial data may need to be merged with internal geographic risk factors, claims and payment data, economic data, demographic data, and so on.
Querying such combinations enables hyperlocal predictive analysis and individualized risk scores for property-tailored pricing – for example, based on the age, location, and materials of a structure that's prone to storm surge or wildfire or based on a farm's crop selection, water usage and, by extension, its resilience against drought. There's a customer-service benefit here also, because the insurer can demonstrate precisely why a policy has been priced as it is, boosting transparency and trust.
Getting there takes data assimilation into data lakes, ideally incorporating systems integrated with enterprise resource planning (ERP) that funnel third-party as well as an insurer's business data into repositories powering predictive-analytics capabilities in both the underwriting and investment sides of the house – in addition to providing for detailed sustainability tracking and reporting.
Third, predictive analytics is also about people. Given the power of predictive-analytics models, underwriters in particular may feel threatened by these models' introduction and proliferation. The maturation and increasing sophistication of AI in predictive analytics will only exacerbate that. So, involve underwriters early. Foster a rapport between analytics specialists and underwriters to make sure analytics enhances rather than hinders underwriter workflow. Show underwriters how predictive analytics can help them improve portfolio profitability, then monitor and encourage their use of these new tools.
Predictive analytics for climate and ESG risks are already out there
Some of the world's biggest insurers are leading the way with predictive analytics for climate and ESG risks. Aon incorporates chronic as well as acute risks in climate modeling to assess commercial customers' risks down to the asset level, covering freeze risk, extreme precipitation, flooding, extreme heat, drought, and more.
Allianz's Climate Adaptation and Resilience Service (CARes) platform includes a self-service tool to translate climate risks into financial and physical loss metrics at portfolio and location levels. On the investment side, its Sustainability Insights Engine (SusIE) embeds climate-relevant data into its portfolio decision-making process.
Also on the investment side, AXA IM analytics provides ESG scores across its asset classes for use by portfolio managers and analysts companywide, and AXA XL's in-house specialists bring in data from catastrophe modeling firms to understand and predict climate risks on both the underwriting and investment sides of the house.
Swiss Re's ESG risk assessment tool ranks potential transactions based on risks and even gives a direct recommendation to abstain. It uses both proprietary data based on country, sector, and a company and project watchlist, and brings in external data from Rystad, SBTi, and others.
These giants are among the pioneers of new approaches to bringing climate and ESG advanced analytics into the cores of their businesses. Others must now follow. Given the stakes of foggy risk assessments in a world where climate disasters are increasingly common, what was once a question of reporting is now one of survival. The first step is to gain command of your data, and there's no time to waste.
