Understanding the exact location of a risk is critical for insurance companies. Yet the process of achieving this can appear complicated and challenging.
To explore some of the solutions that insurance companies can deploy, Digital Journal spoke with Berkley Charlton, VP of Product Management at Smarty, an address data intelligence company.
Charlton outlines the role that geocoding and PUIDs play in revolutionizing peril risk assessment for insurers.
Digital Journal: How is rooftop geocoding essential for insurance companies to accurately price risk in areas prone to significant perils like hurricanes and wildfires?
Berkley Charlton: Understanding the exact location of a risk is critical for insurance companies. The risk profile of a commercial or residential building can vary greatly depending on the actual position of the structure within a property’s boundaries. Rooftop geocoding returns the precise geographic coordinates of each insured structure, allowing insurers to better estimate exposure to specific perils like coastal storm surge, flood, or wildfire risk. This granular approach of pinpointing to precise geocoordinates enhances risk assessment accuracy and supports more accurate pricing models.
DJ: What are persistent unique identifiers (PUIDs)? How do PUIDs and rooftop geocoding intersect to improve the process of pricing risk?
Charlton: Persistent unique identifiers (PUIDs) are consistent numeric codes assigned to an address location. A PUID will remain constant over time, and will not change even when address components such as street names, city names, and postal codes do change.
When all addresses are linked to a PUID, actuaries can more efficiently model and re-model geographic risk by linking many risk factors to the same location linked to a PUID that doesn’t change over time. Underwriters will be able to quickly and accurately price policies to risk data already linked to a PUID, eliminating operational overhead and increasing accuracy. Loss ratios will improve, and risk will be priced more accurately.
DJ: With wildfire regions expanding to Florida, New Jersey, Tennessee, and the Carolinas, how are insurance carriers leveraging address technology to improve ongoing risk assessments in these areas? How frequently do they reassess risk profiles?
Charlton: As the US population grows, it also expands into areas more prone to wildfires. Wildfire risk is very dependent on micro geographies. Consequently, wildfire modeling requires the most accurate geocoding possible to understand and predict risk accurately.
DJ: In what ways are advancements in address technology improving the granularity of data available for underwriting and risk management in wildfire-prone regions? What other tools are available to insurance carriers to improve risk assessment?
Charlton: Advancements in address technology boost data granularity for underwriting in wildfire-prone areas by providing precise property locations and environmental details like vegetation and topography. When addresses are enriched with additional property attributes, including roof type, structure type, number of out-buildings, etc., insurers can assess wildfire risks more accurately based on having a more complete understanding of structures associated with a location.
Beyond address-level rooftop geocoding, spatial analysis with satellite imagery and predictive analytics using real-time data and risk models will improve overall risk analysis and underwriting strategies. These resources help insurers understand and manage potential wildfire impacts more effectively.
DJ: What future innovations in address technology and geocoding do you foresee that could further revolutionize the insurance industry’s approach to the risk assessment of various perils?
Future innovations in address technology and geocoding could significantly transform risk assessment in the insurance industry. Enhanced AI and machine learning integration with geocoding systems could provide real-time, predictive risk analysis by analyzing environmental data such as vegetative growth, slope, and soil conditions. Advanced geospatial analytics can offer dynamic risk mapping, adjusting to changing conditions like severe weather patterns in real-time.
