The role of technology in harnessing hazard data

Since 2017, wildfires have led to a total of $67 billion in insured losses, and today’s estimates suggest that the 2025 fires could increase that by nearly 50 percent. The devastating wildfires in Southern California which started in early January 2025 are but the latest example, with direct economic losses already estimated at more than USD 60 billion along with loss of life and other devastating impacts.

Current insured loss estimates stand at up to $45 billion – representing roughly one-third of the global insured losses for 2024 – and occurred within just the first two weeks of the year. This means that many U.S. primary insurers with direct exposure to these fires will have already used up a significant proportion of their catastrophe loss budgets for the year. Some insurers will likely have also eaten into part of their reinsurance coverage that they purchased only a few weeks ago.

Wildfires are one of the hardest natural hazards to model because humans influence both the hazard (how and where fires ignite and spread) as well as other facts that drive risk, such as exposure (the location and density of communities in fire-prone areas) and vulnerability (the materials and design of structures, or the effectiveness of mitigation measures). 

However, technology is increasingly helping insurers to balance risk across multiple asset classes and geographic areas, by providing underwriting teams with the ability to combine their aggregate positions to quickly understand their inventories of cross-class assets and total potential exposure.

Today’s insurance technology platforms offer a number of different opportunities for insurers to meet this challenge and better understand their cross-class exposures. For example data management platforms like Insurwave allow its users to incorporate multiple sets of hazard data onto a risk map alongside their assets to help underwriting teams to proactively monitor threats and hazard events as they unfold to inform loss modelling scenarios and appropriate event responses for clients in their time of need.

Similarly, AI has proved to be an invaluable tool for boosting the power of imagery analysis by combining current and historical data with new, global third-party data sources and AI-enabled assessments of advanced multispectral imagery. It allows the risk modelling industry to enrich data with physical factors such as sea temperatures, wind strength, and other global external data. Insurers are also making use of predictive analytics, through the application of AI-driven interrogation of multiple data sources to help curate new generative underwriting insights across multiple markets, geographies and accounts. 

On the underwriting side, AI-assisted solutions are transforming the administrative burden of submissions, reducing the turnaround time of submission ingestion and data onboarding from hours to just minutes. Beyond just ingestion, this technology, deployed by leading providers like Insurwave, can also provide added enrichment of bound portfolios, giving underwriting teams deeper insights as they write or renew business. For example, this could include the ability to leverage third-party wildfire data sources that go beyond simple point-in-time exposure views. Instead, they could provide a more holistic, event-level exposure picture—incorporating shape files for event footprints, along with buffer zones to indicate likely direction of travel.

As development of this technology continues, it’s clear that the deeper insights into these hazards is key to the ability of the insurance industry to support people and businesses to deal with the financial and human impact of flooding, wildfires and other perils of the changing climate. So, looking beyond today, what other techniques are being considered to help further improve the ability of insurers and their underwriting teams to monitor and mitigate climate risks?

The volatile effects of climate change have left many traditional insurers unwilling to underwrite natural hazards like the recent wildfires, however, like in many instances of cat risk management, parametric insurance products are helping to fill the gap left by more traditional indemnity-based insurance solutions, relying on hazard, environmental and climate data to verify the payout conditions.

However, as with most solutions, it is often a combination of several different approaches that proves to be the most successful in solving a problem. In a recent interview, Euro-Mediterranean Centre on Climate Change (CMCC) researcher Guido Rianna called for holistic approaches to combatting the effects of climate change that combine risk monitoring, modeling, financial tools, and local knowledge.

“Insurance should promote territorial protection, increase awareness, and integrate advanced monitoring systems. For example, insurers could lower premiums for policyholders who invest in risk mitigation, such as better land management. This would benefit both the insured and the insurer,” Rianna explained.

According to the researcher, there are currently two case studies being developed in Italy to this end that involve a combined approach. One in Campania – where a semi-parametric insurance product that provides immediate post-event financial support is being tested; and another in Sardinia – where a community-based insurance model is being developed so that the insurer does not interact with individual farmers or businesses but with a collective entity that represents multiple stakeholders.

Ensuring Insurers have access to precise hazard data is critical in shaping the future of wildfire risk management as it provides the foundation for accurate risk assessments and confidence in the quality of data underwriting teams are basing their decisions on. 

As we have shown there are solutions out there that can integrate real-time data like weather forecasts, satellite imagery, and fire detection sensors with hazard data to allow for early wildfire detection and prediction. However, technology is only one piece of a larger collaborative effort that combines financial instruments with technology and governance to ultimately enable better preparedness and minimise the impact of wildfires in vulnerable regions.