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Geospatial Data to Support Climate Resilience in Insurance

See how AI-powered geospatial data is helping P&C insurance providers enhance climate resilience.

Natural catastrophes caused approximately $250 billion in global damages last year, resulting in an estimated $95 billion of insured losses. The growing frequency and intensity of extreme weather events are posing challenges for property and casualty (P&C) insurance providers. In response, some insurers have chosen to suspend coverage in specific regions as they grapple with the evolving risks.

However, AI-powered geospatial data is creating a unique opportunity to help P&C carriers enhance climate resilience and stay competitive in this evolving landscape. Continue reading to learn how.

Climate-related risks for P&C Insurance

Property and casualty insurers are significantly impacted by climate events such as hurricanes, wildfires, and floods, as these events can cause extensive damage to insured properties, resulting in large claim payouts. As the frequency and severity of these events increase due to climate change, so does the risk for insurers. Consequently, insurers have to reevaluate and potentially adjust their risk models and pricing strategies to adapt to the evolving climate landscape. The ability to underwrite and reinsure in this changing environment is crucial to maintaining a competitive edge, and high-precision geospatial data plays a pivotal role in doing this.

A photo showing a 2020 wildfire in Monrovia, California. The 2020 California wildfire season burned more than 4 million acres of land and caused billions of dollars in damages.
A photo showing a 2020 wildfire in Monrovia, California. The 2020 California wildfire season burned more than 4 million acres of land and caused billions of dollars in damages.

Climate resilience challenges from inaccurate geocoding 

It is critical for P&C insurers to have the precise location of every building that they insure to accurately evaluate risk. Insurance providers do this through a process called geocoding. Unfortunately, a lot of geocoders on the market are inaccurate. Most geocoders use a parcel centroid approach, placing a geocode at the central point of a land parcel, or a street-level geocode, which provides an approximate location on a street rather than accurate coordinates. Insurance companies using these types of geocoders face challenges in obtaining precise coordinates for a structure associated with a policy. This presents a significant problem because an exact location is crucial for high-precision risk assessment. For example, if a carrier does not have a precise location of a building and accurate information about its surroundings, it will not be able to measure how far a structure is from a hazard like a flood zone, and thus how susceptible it is to possible flood damage. 

The image below illustrates that an inaccurate geocoder can place a parcel centroid miles away from the actual structure, indicating a property as low risk. In reality, the building footprint is situated within the flood zone, meaning it is high risk. This leads to policies being underpriced, exposing the carrier to an increased risk of unforeseen flood claims. Conversely, an incorrect geocoder could place parcel centroids within a flood zone, when in reality, the building footprints are outside the flood zone, meaning the structures are actually at a lower risk of flooding. In this scenario, the imprecise geocoder would lead to policies being evaluated as high risk, causing inflated policy costs. This may encourage customers to migrate to a competitor providing better rates, leading to an increase in customer turnover.

A real-world scenario illustrating underpricing and overpricing risk from an inaccurate geocoder in Richmond Hill, Georgia. Both of these scenarios expose carriers to risk.
A real-world scenario illustrating underpricing and overpricing risk from an inaccurate geocoder in Richmond Hill, Georgia. Both of these scenarios expose carriers to risk.

Building footprints: the foundation of climate resilience for insurance

Ecopia AI’s (Ecopia’s) Building-Based Geocoding addresses challenges related to inaccurate geocoding. Ecopia’s AI-enabled technology ingests and processes up-to-date geospatial imagery to produce latitude/longitude coordinates that correspond to high-precision building footprints and rooftop-level geocodes that assign geographic coordinates to specific building structures. This technology currently enables 7 of the top 10 US P&C carriers to determine the exact location of each insured building in their portfolio and provide location and property information needed to accurately assess risk. 

Ecopia also offers 3D building data, providing insurers with a comprehensive understanding of the true extent of a building which further strengthens risk assessments. Ecopia’s AI-based systems frequently ingest fresh imagery to help carriers identify changes like structural damage or home additions allowing insurers to assess the evolving risk profile of a property over time.

A sample of Ecopia’s AI-powered Building-Based Geocoding data, derived from high-resolution imagery in Franklin Square, New York.
A sample of Ecopia’s AI-powered Building-Based Geocoding data, derived from high-resolution imagery in Franklin Square, New York.

The next section of this blog explores how Ecopia’s AI-based geocoding and building footprint data are helping carriers strengthen climate resilience in the context of risk management and underwriting, claims, and master data management.    

Insurance operations to bolster climate resilience

Risk assessment and policy underwriting

AI-powered geospatial data can quickly give insurers the information they need to make decisions related to risk assessment and policy coverage, terms, and pricing. As previously mentioned, assessing a property's distance from potential hazards, like flood zones or nearby structures, is a crucial aspect of risk evaluation. Insurance providers integrate data on diverse hazards such as wildfires, earthquakes, and wind when conducting their risk assessments. They combine this information with details about property attributes, including building materials, occupancy, and other relevant factors, to formulate a comprehensive risk profile for each policy.

AI-based building footprints and rooftop-level geocodes empower insurers with the high-precision data needed to assess risk associated with a policy, preventing carriers from relying on approximate locations to calculate risk. In the context of enhancing climate resilience, this data helps support a more sophisticated understanding of, and proactive approach to, climate risk. By leveraging AI-generated building footprints, insurers can efficiently identify climate risks and vulnerable areas which can bolster strategies aimed at establishing resilient communities, providing benefits for both the insurance provider and the communities involved.

Claims management 

AI-driven geospatial data also plays a crucial role in examining claims. When a policyholder makes a claim, it could be very difficult for an insurer to accurately determine the impact of an event on a structure, like a natural catastrophe, if they are unsure of the exact coordinates due to inaccurate geocodes. This location intelligence is imperative because it forms the basis for property valuations and replacement cost estimates, plus plays a crucial role in ensuring precise and reliable assessments for effective claims processing.

Without high-precision data, an insurer may overpay or underpay after an event, both of which can result in risks. AI-based building footprints and rooftop geocodes give insurers the high-precision data they need to effectively assess and validate claims by accurately determining a property's location and assessing the impact on individual structures during an event. With precise information, insurers can better evaluate the impact of climate events on insured properties and make informed decisions related to claims and future coverage and pricing. With this nuanced understanding of the impact of climate events on individual structures,  insurers can collaborate with policyholders, local communities, and governments to develop customized mitigation strategies, like reinforcing buildings, to strengthen climate resilience for the future. 

Master data management

Managing data in our rapidly changing world is complex. Numerous property and casualty (P&C) insurers struggle to establish a sustainable master data management (MDM) strategy, leading to difficulties in maintaining a cohesive database throughout their organizations. The root of these challenges lies in various departments overseeing separate data sources with conflicting information about properties. 

Despite these challenges, comprehensive MDM remains crucial for insurance providers aiming to enhance climate resilience. An up-to-date and centralized database relating to policyholders, insured assets, and geographical locations is essential for insurance companies to assess and quantify climate-related risks effectively. Ecopia’s AI-powered Building-Based Geocoding solutions provide unique identifiers for buildings, parcels, and addresses. This provides insurers with a comprehensive data management solution to streamline processes, assist in scenario planning and modeling, and better understand the climate risk exposure of their portfolio. 

Get started with AI-powered data to enhance P&C climate resilience 

P&C carriers can play a unique role in mitigating the consequences of natural disasters and actively contributing to prevention efforts that impact their bottom line. Ecopia’s AI-based mapping provides a scalable and cost-effective solution to keep foundational property data up-to-date, enabling insurers to better understand, mitigate, and adapt their strategies to new climate risks and stay competitive in our changing world. To learn more about Ecopia’s insurance solutions get in touch with a member of our team.

Learn more about Ecopia's insurance solutions

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