A few facts to consider: fires account for 28% of non-catastrophic home insurance losses, but fewer than 3% of all homeowner claims. And when fires do happen, the average loss is more than $66,000.
Unlike other types of homeowner losses, fire losses rely on a community’s fire response capabilities. These factors underscore the importance to Carriers of accurately measuring a community’s fire response proficiency. The ideal solution includes the ability to more precisely determine fire location, and incorporate other data points that impact response effectiveness, such as driving time, historic response time, or percentage of time spent by a fire department on medical or other non-fire events.
Unlike other insurance perils, fire risk is typically classified with simple, non-analytic rules or expert opinions. A more effective tool in fire risk assessment is predictive analytics, which turns quantifiable data into actionable insights for more precise pricing and underwriting. A data-driven product can exceed even the best expert’s ability to offer consistent empirical results. The data products that exist don’t go beyond scoring fire-fighting preparedness and don’t employ a model that would help insurers optimize incremental performance.
Data-driven solutions like the LexisNexis Fire and Disaster Response Score can help carriers provide more accurate and equitable prices for homeowners insurance. By converting quantifiable data into actionable intelligence, this model can estimate an average future loss severity based on characteristics, even when each claim will vary widely from this estimate. The solution goes beyond scoring fire-fighting preparedness and allows insurers to optimize incremental performance.
Data-driven products offer many benefits over the current practices for scoring fire risk, including:
- The ability to leverage fire loss history in a given area to predict future fire loss propensity
- Lower cost – because it does not require an army of surveyors or non-critical paper processing, and
- A more accurate assignment of premium and loss – because it does not rely on data tied closely to tax revenue and property values.
By leveraging data that is predictive of loss—such as true driving time, actual response time and neighborhood-level loss statistics—all of which are free of affluence bias, carriers can enable more sophisticated strategies for profitable pricing.
Download Disrupting Fire Risk Underwriting With a Data-Driven Solution and learn how predictive analytics enables carriers to more effectively assess, price and underwrite fire risks by turning quantifiable data into actionable insights.