A Clearer View of Location Risk
Location information is critical for assessing risk and underwriting commercial property insurance effectively.
Most commercial property underwriters today gather risk-specific location information manually. In addition, they are limited by having incomplete data on existing exposure and property-specific peril by location when they underwrite.
Fortunately, access to geospatial technology and new data sources are allowing carriers to improve the breadth and efficacy of risk peril data in commercial property underwriting.
Access to raw data is useless if you can’t interpret it. New geospatial technology solves this problem by converting raw peril data into machine-readable risk scoring indices that can be integrated into an automated underwriting workflow.
Peril-specific risk scoring indices can be produced by taking historic peril-specific data on an address and assigning it number from one to five, indicating the intensity of its peril exposure. For example, a property that has a long history of hail events will have a hail score of five. A property that has little or no hail event history may have a hail score of two.
Carriers can then establish thresholds based on these peril indices so that they can easily sort out which potential risks are a definite yes, definite no, or require further scrutiny. For example, a carrier may not have the appetite for property with a wind peril score of four or higher, and it may want to apply additional underwriting attention on a wind peril score of three.
Having these tools in place positions the carrier to implement better location specific underwriting along with a process we’ve called Strategic Visualization.
How Strategic Visualization Works
Underwriters don’t have the time to visualize every property across dozens of parameters. Strategic visualization helps by simply automating a process that leverages risk-specific indices to identify risks that require additional underwriter attention.
An automated scoring engine can use indices across different risks to identify those that are below acceptable thresholds and automatically send them through to bind. Risks that exceed underwriting thresholds are referred for further action and underwriter attention.
Scoring can be fine-tuned to match organizational risk tolerances at the SIC or business category level. Carriers can implement peril-specific indices or thresholds by business type into existing underwriting workflows, visualizing only the risks that are above the threshold and, in turn, improve productivity and their bottom line.
A carrier underwriting a car dealership, for instance, may set a lower risk threshold for hail while jewelry store underwriting may necessitate a lower threshold for theft.
What types of risk-relevant geospatial information should underwriters look at? The options are constantly expanding, but the following five categories of geospatial data should be considered:
Location-Based Risk Factors are business-related information at the geocode level. It is used to assess a location’s risk level based on the neighborhood business climate, including bankruptcies, foreclosures, vacancies, business failures, business creation and business change rates. Location risk factors help the underwriter understand the type of neighborhood and environment in which the business is operating.
Natural Hazard Data provides insight on the propensity and potential future damage from natural hazard perils by location. These perils include floods, high winds, tornadoes, hail, brush fires and earthquakes. While the availability of natural disaster data is not new, the ability to distill this data to a set of property specific indices, along with analysis of existing exposure and visualization on a single platform is new.
Firmographic Data assesses the types of businesses that surround a particular property location that can have impact on risk. For example, a day care located next to a bookstore and elementary school will have a different level of risk than a day care sandwiched between a liquor store and a pawn shop.
Historical Loss Data is perhaps the most effective predictor of future risk. Aggregated loss history indices from industry-wide contributory claims databases reveal valuable insights regarding historic theft, wind, hail, lightening, fire, water and liability claims by location.
Existing Policy in Force Data provides the current exposure context based on policies already on the books and allows the carrier to assess whether underwriting that new business — in that particular location — will create exposure beyond their appetite for risk.
Real time access to geospatial insights at point of underwriting can significantly improve the commercial underwriting process. Underwriters only touch those risks that require more attention. At any point, carriers can customize and fine tune the technology to better match their risk tolerance.
Geospatial visualization is shaping the future of commercial underwriting. Carriers that incorporate the technology in their underwriting processes will gain a significant competitive advantage.