Geospatial Visualization Tool for Insurance Asset Risk Assessment
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Solution Overview
Problem
Evaluating the suitability of insurance coverage for physical assets is challenging due to imprecision in predicting future damage and changes in asset conditions, leading to the need for frequent re-evaluation, which is time-consuming and inefficient when relying on manual reviews of individual policies.
Innovation Solution
A geospatial visualization and query tool that links datasets with geospatial data and image data, allowing users to navigate and assess assets through a graphical user interface, applying custom filters and ranking algorithms to prioritize assets based on exposure risk, facilitating subjective and objective assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reviews of individual policies are used to evaluate insurance coverage suitability, then detailed assessment can be performed, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system segments the large dataset of insurance policies into smaller, manageable groups based on geospatial clustering. Policies are divided into clusters representing different geographical regions, allowing reviewers to focus on specific areas rather than examining every individual policy manually. This segmentation maintains assessment accuracy while significantly reducing the time required for comprehensive review.
Solution Approach 2:
The system creates visual copies and representations of policy data through geospatial maps and cluster visualizations. Instead of reviewing raw policy documents, reviewers interact with visual copies that display policy concentrations, risk patterns, and geographical distributions. This copying approach preserves the essential information needed for accurate assessment while enabling faster visual analysis.
2Reliability
If comprehensive data verification and validation are performed on large datasets, then data integrity is improved, but the complexity and time required for manual processing increases
Solution Approach 1:
The system replaces manual mechanical review processes with automated computational methods. Geospatial algorithms automatically process and validate large datasets, performing data verification and validation without human intervention. The system substitutes manual complexity with automated computational complexity, maintaining data integrity while reducing the burden of manual processing.
Solution Approach 2:
The system changes the parameters of data processing by transforming raw policy data into geospatial coordinates and visual representations. This parameter transformation allows for automated validation and verification through spatial analysis, reducing processing complexity while maintaining reliability. The change from tabular data to geospatial parameters enables more efficient data integrity checks.
Data Source
AI summary
A geospatial query and navigation process generates a first filtered result comprising assets that satisfy a user generated query, where each asset includes a geospatial location and an exposure rank that estimates a risk associated with the asset based at least in part, upon a policy limit of a policy associated with the asset. The process also generates a current view of a geographical map, and positions indicia on the current view of the geographical map representing assets within the first filtered result according to their geospatial location that are also located within the current view of the geographical map and that also satisfy a predefined exposure rank requirement. The process modifies the current view of the geographical map to alter displayed geographical boundaries responsive to a user entered selection, whereupon the displayed indicia representing assets is updated according to the modified view of the geographical map.