How spatial analytics support insurers coping with climate change
Geographic data and spatial analytics can create competitive advantages by helping insurers better manage losses.
Today’s P&C insurers are facing a variety of factors impacting their strategies such as fluctuating climate conditions, evolving preferences for coverage options, and new risk assessment. To remain competitive, meet the evolving needs of their insureds, and react to changing market trends in real-time, insurers must prioritize modern, cloud-based spatial analysis and location intelligence.
Geographic data and spatial analytics can create competitive advantages by helping insurers manage losses more precisely and efficiently. In the past, spatial analytics data was hard to understand and utilize due to its fragmented structure and ongoing difficulties in integrating geospatial analysis tools and data into existing technology stacks.
However, as new technology— including artificial intelligence (AI) — enters the location intelligence space, insurers can more easily incorporate such tools into their modern cloud technology stack. This empowers insurers to glean insights from more diverse datasets, analyze at greater speed and scale, and ensure that insights are accessible to more users across the business.
Democratization of geospatial analytics
With the introduction of cloud-native and AI-enabled spatial analytics technologies, more insurers are capable of accessing advanced analyses that can help guide their approach to coverage and policy development. Rather than relying on separate tools with expensive licenses that require specialized expertise, insurers can now easily fit spatial analytics into their existing technology stacks at a reasonable price.
The introduction of AI innovations that perform spatial analysis using natural language (versus coding languages like Python or JavaScript) also means that anyone can access and understand spatial data, not just those who have been specially trained. For example, with the right platform, insurers can input a simple location-based query — such as the risk of flooding in a certain neighborhood — and receive a detailed map and analysis of what they need. This leads to more precise, real-time results that can be accessed by an entire organization versus a few Geographic Information System (GIS) experts on their team.
Increased reporting precision leads to more accurate risk assessment.
As the availability and accessible amount of geographic data increases, insurers can more accurately and quickly evaluate risk and predict market trends, especially related to climate change.
For example, data on where past wildfires have taken place can be analyzed to create a risk index that allows decisionmakers to assess the potential of fire occurrences in a certain area. Spatial analysis not only accounts for the location where fires take place; it also incorporates data points on underlying characteristics of a geographic area, population, elevation and soil moisture to provide the most accurate assessments of risk. Additional insights, like rates of occurrence and timing, can be gleaned from this data as well, all helping insurers more effectively determine the best premiums and coverage plans for homes and businesses at risk of wildfires.
This type of analysis can also be repeated for other natural disasters, like floods or hurricanes. Better data and analysis lead to improved forecasting. In a world where these types of events are on the rise, insurers must have the best spatial analysis available to stay competitive.
Improved claims management
Spatial analysis can improve how insurers assess claims in addition to modeling particular types of risk. When hurricanes or fires occur, satellite imagery can be used to determine the extent of impact to customers. Combining this with the geographic data discussed above can ensure that property claims accurately reflect the impact of unfortunate events to better serve policyholders.
For example, Nationwide uses geospatial data to better understand the severity of a natural weather event in a particular coverage area. The resulting analysis of damage allows them to confirm claim validity and adjust their home insurance policy structures based on the impact of that specific event and the geographical likelihood of it happening again.
Moving toward a future of real-time, dynamic engagement
With the further development of AI, insurers will also be able to further automate their analyses. Stakeholders can expect AI to enable continuous monitoring and real-time assessment of potential threats to property and people.
For instance, if a hurricane is approaching a customer’s home or business, AI tools will be able to constantly collect data and provide updates on the hurricane trajectory, allowing insurers to share helpful and timely information with their customers on how to best prepare and protect their property.
In an increasingly complex and competitive insurance landscape, the integration of spatial analysis and location intelligence is transforming how property and casualty insurers assess risk, predict coverage needs, and develop tailored policies. These advanced tools are not just enhancing accuracy and efficiency — they are essential for gaining a strategic edge.
Insurers that integrate modern spatial analytics tools into their regular technology stacks will be the ones best positioned to meet the evolving demands of tomorrow’s customers. Integrating geographic data into actionable insights will help drive smarter decisions, better outcomes, and sustained leadership across the insurance industry.
Javier de la Torre, Founder and CSO, CARTO, a cloud-native location intelligence platform for spatial analytics that uses geospatial data to enable insurers to provide affordable coverage and reduce risk to communities. These opinions are the author’s own.
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