Transforming fire risk assessment with accurate data & advanced models
The growing availability of external data and advances in risk modeling can make a difference in assessing and mitigating these losses.
Fire-related events represent a significant portion of an insurer’s claims, upwards of 24% of expenses according to the Insurance Information Institute.
According to the National Fire Protection Association (NFPA), there were approximately 1.3 million residential fires resulting in an estimated $14.8 billion in losses in the U.S. For commercial properties, the percentage of claims that are fire-related is typically lower than for residential properties, but the damages are still significant and result in higher claims, as well as added business interruption losses. According to the NFPA, there were approximately 99,000 commercial structure fires resulting in $2.6 billion in property damages.
The growing availability of external data and advances in risk modeling can make a marked difference in assessing and mitigating these losses. Given the risks and damages, and the availability of new data and technologies, insurers should reassess their fire risk assessment strategies and fire suppression rating schedules.
The most common fire risk applications on the market rate fire protection of properties, in large part, based on their distance to fire stations, on a score of 1 to 10, and do so mainly at the community level, giving all properties in an area the same rating. These traditional tools rate risk based on criteria including (a) the availability of fire hydrants, (b) the quality of the local fire department, and (c) the water supply in the area. These models place emphasis on the distance to fire stations with little consideration for drive time from the fire station to the property location.
With the availability of new data and risk models, the potential exists to make a much more informed and scientific assessment based on data points that are more highly correlated with fire damage to a property and thus more accurately predictive of potential loss.
Society, and the insurance industry, are in the midst of an information revolution. The scope of available data is rapidly expanding. Yet, fire protection capabilities are primarily evaluated today according to the same limited data used in 1972: a location’s fire district and closest estimated water source.
Leveraging modern Geospatial Hazard Ratings — which draw upon satellite data, Geospatial Information Systems, and AI — insurers now have the capability to assess the level of fire suppression at the individual property level. This allows insurers to make more informed, precise decisions about risk exposure. Underwriters and actuaries can now assess the potential risk of fire damage for individual locations — every individual and commercial property location in the U.S. — rather than for the entire district, census block, or city.
Modern fire risk models can leverage more precise, location-specific data for things like ‘distance to water’ and ‘drive time to fire station’ to personalize the fire risk for each location.
The importance of precise data in fire risk assessment
Let’s examine why this matters. Fire hydrant proximity to a property is the most significant indicator of the potential for fire severity and loss. Traditionally, this metric is self-reported and merely guessed at by the customer to the insurer. It is also often only a binary choice of under or over 1,000 feet to the home.
The time needed to establish a water connection by the fire department is one of the most critical determinants in fire severity and loss. Every second counts. For instance, a home under 250 feet from a fire hydrant burns with 9% less fire severity than a home located 250-500 feet away. The fire loss severity is nearly two times less for homes located less than 250 feet from a fire hydrant versus homes located beyond 1,000 feet from a fire hydrant (or water source).
Thus, getting more accurate data on the fire suppression capabilities in proximity to specific addresses has a direct correlation to the potential fire severity and corresponding damages and losses.
Benefits of modern fire risk models
Enhanced underwriting: Modern fire risk models empower underwriters to identify potential risks and opportunities more effectively during the underwriting process. By utilizing accurate and comprehensive data, underwriters can make informed, accurate, and immediate accept/decline decisions. This improved underwriting capability ensures that insurers have a better understanding of the fire risk associated with each property.
Improved pricing & profitability: Precise fire risk assessment allows insurers to set premiums that accurately reflect the fire risk of individual properties. Instead of relying on generalized classification systems, modern fire risk models enable insurers to create more refined risk segments. This means insurers can offer fire policies to properties that may have been considered too risky by other systems, resulting in more comprehensive coverage options. Additionally, by aligning premiums with the actual risk, insurers can optimize pricing strategies, ensuring fairness and profitability.
Competitive advantage: Adopting modern data elements and advanced risk models provides insurers with a competitive advantage in the market. These tools offer additional risk insights on properties, enabling faster underwriting decisions and more accurate pricing for policyholders. By leveraging cutting-edge technologies and robust data sources, insurers can stay ahead of the competition, attract more customers, and provide enhanced value to policyholders.
Sustainable customer service: The utilization of modern and accurate data elements, coupled with advanced risk models, allows insurers to assess fire risk at a granular level. This enhanced accuracy enables insurers to serve more customers sustainably, making insurance coverage more accessible and available to a wider range of properties. By providing tailored coverage based on precise risk assessments, insurers can contribute to a more insurable world, fostering greater resilience and protection for individuals and businesses.
In conclusion, the adoption of modern data elements and updated fire risk models brings a multitude of benefits to insurers. These models enable enhanced underwriting practices, improved pricing strategies, and a competitive edge in the market. By leveraging accurate data and advanced risk assessment tools, insurers can assess fire risk more precisely, cater to a wider customer base, and contribute to a more insurable world.
John Siegman is the co-founder of Hazard Hub, a property risk data company that was acquired by Guidewire in mid-2021. He is now a senior executive at Guidewire helping to lead the direction of the HazardHub solution and guiding P&C insurance clients in innovating their data integration into critical processes.
Opinions expressed here are the author’s own.
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