Certified Location Data Aggregation for Insurance Risk Precision
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Solution Overview
Problem
Location data is often imprecise, incomplete, and non-standardized, leading to hidden location-based attributes that hinder business decisions, particularly in insurance and risk-based product quotations.
Innovation Solution
A system for collecting, managing, and utilizing certified location data by aggregating, filtering, and standardizing location information to define unique geo-referenced points or polygons, enabling accurate identification and relationship determination of locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If location data is collected from multiple sources to improve completeness, then the quantity of location information increases, but the precision and reliability of location data deteriorates due to overlaps and conflicts
Solution Approach 1:
The patent segments location data into multiple hierarchical levels (e.g., address level, block level, census tract level, metropolitan statistical area level). This segmentation allows the system to store and manage location information at different granularities, enabling users to query at the appropriate level of detail while maintaining data quality. Each segment is validated and standardized independently, resolving conflicts between overlapping data sources.
Solution Approach 2:
The patent introduces location factors as intermediary elements that mediate between raw location data and business applications. These location factors (e.g., distance to nearest hospital, crime rate, property tax rate) are calculated based on certified location data and serve as standardized intermediaries that resolve conflicts between different data sources by providing unified, validated metrics.
2Reliability
If location data is standardized to improve data quality, then the reliability of location information increases, but the complexity of data processing increases
Solution Approach 1:
The patent creates a universal location data framework that serves multiple functions simultaneously: data validation, standardization, certification, and analysis. The location factor model provides a multi-functional approach where the same standardized structure supports various business applications (insurance underwriting, risk assessment, marketing) without requiring separate processing systems for each application.
Solution Approach 2:
The patent transforms location data from unstructured text formats into standardized parametric forms with defined attributes and validation rules. By changing the parameter representation (e.g., converting address strings into structured location records with latitude/longitude coordinates, block identifiers, and census tract codes), the system achieves reliable standardization while managing complexity through consistent parameter transformations.
3Measurement precision
If detailed location information is collected to improve risk assessment accuracy, then the measurement precision of location attributes increases, but the difficulty of detecting and measuring location relationships increases
Solution Approach 1:
The patent adds spatial dimensions to location data by incorporating geographic coordinates, block geometries, and spatial relationships between locations. This dimensional enhancement allows the system to detect and measure location relationships (e.g., proximity to hazards, distance to services) automatically through spatial queries and geometric operations, rather than manually analyzing text-based address data.
Data Source
AI summary
Location information may be gathered, managed, stored, and/or otherwise utilized to determine unique geo-referenced locations. The geo-referenced locations may be utilized to inform various processes and decisions such as insurance underwriting, risk assessment, pricing, and risk/loss control.