We recently attended the inaugural Geospatial Risk Summit, where geospatial professionals from both the private and public sectors joined to discuss a wide range of risk-related topics, including the use of data to assess risks across entire portfolios with the goal of educating property owners and developing mitigation strategies that drive resilience. In this blog, we’ll summarize what we learned from these compelling conversations about the future of geospatial data in the insurtech industry and provide resources for conducting large-scale risk assessments for strategic prediction and prevention.

Limitations of data modeling

For the past three decades, the insurtech industry has largely relied on data models to understand property risk and inform underwriting, claims management, and overall portfolio strategy. Geospatial data and location intelligence are integral components of catastrophe (cat) modeling, as they provide detailed information about the hazards and conditions impacting individual properties.

Cat modeling is a critical component of the modern property and casualty (P&C) insurance industry, but there are still some limitations when it comes to fully understanding - and addressing - property risk. Although data models are incredibly valuable for developing highly detailed quantifications of risk profiles for individual properties and entire portfolios, they are not currently leveraged to their full potential as a proactive tool for policyholders, carriers, and the public sector alike. Most cat modeling applications are focused on quantifying, accepting, and responding to risks rather than predicting and preventing them in the first place. 

While data models are extremely valuable for quantifying individual property risk and driving strategy for P&C insurers, there is now an increasing interest in understanding portfolio risk at a large scale to inform prediction and prevention strategies that benefit both insurers and their customers.

The future of insurtech: holistic risk assessment that informs prediction and prevention strategies

At the Geospatial Risk Summit, thought leaders from the public and private sectors acknowledged that cat models are valuable and will remain used for critical analytics in underwriting, reinsurance, claims management, and more. However, they also pointed out that many insurers and government organizations are looking to usher in a new era of risk assessment by partnering, educating people on risk, and offering solutions to alleviate it, ultimately bringing down costs for carriers, relief agencies, policyholders, and local communities. 

As with cat modeling, this strategy of collaborative prediction and prevention hinges on one important component: data.

Data-driven collaboration between the private and public sectors

When high-quality geospatial data is available to all stakeholders and not siloed within disparate organizations, risks can be accurately predicted and prevented. By working together to approach risk more holistically, insurance carriers, government agencies, and property owners can leverage insights from geospatial data to address risks proactively. 

Instead of only using data to power cat models that quantify risks for very specific scenarios, carriers can leverage it to see what types of perils are most costly to their business, identify the policyholders most at risk, and develop mitigation strategies with the help of the public sector.

Community education and engagement

Data availability provides organizations in both the public and private sectors opportunities to educate communities and individuals on the risks they face and how to mitigate them. For example, access to data will help prospective property buyers understand what type of risk they are taking on and enable existing owners to monitor changing risk profiles. Likewise, public sector leaders and P&C carriers can preemptively engage with those most at risk to help get ahead of future hazard events.

Prevention programs and incentives

When risks are identified proactively, insurance providers and local communities can develop programs to enhance resilience to specific perils and reduce the likelihood of damage from a hazard event. This can look like offering financial incentives for policyholders that address issues on a property that ultimately lower its risk score, or subsidizing the work needed to do so.

Hazard response

Of course, no amount of data will prevent all hazards from occurring. However, response teams that are equipped with comprehensive, accurate, and up-to-date data about the impacted area will be better able to triage the situation and begin the rebuilding process. Whether it’s a claims adjuster determining how much a P&C carrier should pay out after an event or an emergency management agency allocating resources, stakeholders with access to data can bolster resilience for the future while also responding to current perils.

High-precision geospatial data for risk assessment

When hazards occur, they don’t just impact policyholders and their insurance agencies; they affect entire communities, from business operations to transportation infrastructure to emergency management and everything in between. At Ecopia AI (Ecopia), we work with geospatial professionals across the public and private sectors to produce high-precision vector data for risk assessment, including geocoded building footprints, land cover, impervious surfaces, and 3D features. Here are some examples of ways Ecopia data is powering prediction and prevention strategies for P&C carriers and government organizations alike.

Wildfire risk

Building footprint and land cover data helps insurers and government agencies calculate important metrics such as a structure’s distance to the nearest water body or nearby buildings, which help quantify wildfire risk and provide actionable insights to increase resilience.

Flood risk

Impervious surface data delivers insights into how water will flow in a storm event, as artificial land cover like parking lots, roads, buildings, and sidewalks lead to higher levels of flooding than natural features and must be factored into flood models.

Wind risk

3D building and tree data enable stakeholders to identify properties most at risk of damage during wind storms, guiding resilience decision-making for insurers, policyholders, and communities.

Structural risk

Geospatial data about property structures themselves help policyholders address any structural risks and damages that may elevate their risk profile.

Ecopia’s AI-based mapping systems extract data with 95% or higher geometric accuracy at scale from current, high-resolution imagery to give private and public sector organizations a true digital representation of the physical world. Our building footprint data is appended with rooftop-level geocodes and a system of unique identifiers to make it easy to pinpoint properties and develop risk mitigation strategies. We’re here to support the data needs of risk professionals in the P&C insurance industry and government organizations as they increasingly work together to predict and prevent hazard events rather than just respond to them after the fact. 

Get in touch with our team to learn how Ecopia’s data can accelerate your organization’s risk assessment goals.