Property Risk Rating Segmentation by Peril
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Solution Overview
Problem
Current methods for pricing and underwriting property insurance use aggregate loss costs across all perils, failing to accurately assess individual risk exposure and offer premiums aligned with specific peril combinations, leading to potentially higher losses for some policyholders.
Innovation Solution
The method involves subdividing a territory into peril zones based on historic loss costs for each peril, using a data processor to sort and create regions with roughly equivalent loss costs, allowing for more accurate risk assessment and premium calculation on a by-peril basis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If aggregate loss costs across all perils are used for pricing and underwriting, then the pricing process is simple and comprehensive, but the accuracy of individual risk assessment and premium pricing deteriorates
Solution Approach 1:
The patent segments the aggregate loss cost data into separate peril-specific loss cost categories. Instead of using a single aggregate loss cost figure, the system divides losses by peril type (e.g., wind, hail, fire, water) and creates separate rating zones for each peril. This segmentation allows the system to maintain computational simplicity while significantly improving the precision of individual risk assessment by matching specific peril losses to specific peril coverage.
2Productivity
If aggregate loss costs are used to determine premiums for all policyholders, then the underwriting process is efficient, but the ability to identify and price specific risk exposure deteriorates
Solution Approach 1:
The underwriting process is segmented into peril-specific evaluation components. The system creates separate rating zones for each peril based on geographic and risk factors, then combines these segmented assessments to determine final premiums. This maintains underwriting efficiency by using automated zone-based processing while preserving peril-specific risk information that would otherwise be lost in aggregate calculations.
Solution Approach 2:
The patent applies local quality by creating peril-specific rating zones that reflect local risk characteristics for each peril type. Instead of applying a uniform aggregate loss cost across all regions, the system creates locally tailored rating zones for wind, hail, fire, and other perils based on actual loss data and risk factors specific to each region and peril combination, thereby preserving and utilizing peril-specific risk information.
3Device complexity
If a single average loss value is used for all policyholders, then the pricing structure is simple and uniform, but the accuracy of reflecting individual customer risk exposure deteriorates
Solution Approach 1:
The pricing structure is segmented into multiple peril-specific rating zones rather than using a single uniform rate. Each peril type has its own rating zones created based on geographic location, loss cost data, and risk factors. This segmented approach maintains relative simplicity by using zone-based pricing while dramatically improving the accuracy of reflecting individual customer risk exposure through peril-specific zone assignments.
Solution Approach 2:
The system changes the pricing parameters from a single aggregate loss value to multiple peril-specific loss cost parameters. Each peril type has its own loss cost data and rating zone parameters, allowing the system to adjust pricing parameters individually for each peril based on actual loss experience and risk characteristics, thereby improving the accuracy of risk exposure reflection while maintaining manageable structural complexity.
Data Source
AI summary
Systems and methods provide optimized property risk ratings and, more particularly, optimized property risk ratings defined by evaluating ratings on a by peril basis. Systems and methods also price insurance products and underwrite insurance products using risk data that has been optimized on a by peril basis. A territory is subdivided into a plurality of regions determined in accordance with at least one predetermined factor. Loss costs representing historic costs for various insured perils are retrieved from a computerized database using a data processor. The loss costs are sorted on a per-peril basis for each of the regions using the data processor. Peril zones are created for each peril that represent related regions in which loss costs for a particular peril are roughly equivalent.