Geospatial Financial Benchmarking via Iterative Radius Queries
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Solution Overview
Problem
Small businesses face challenges in finding reliable financial benchmarks due to the lack of geographically relevant data and the reliance on personal knowledge of financial advisors, as conventional methods lack scalable and real-time data sets for entity attribute comparison.
Innovation Solution
A system and method that utilize geospatial-based queries to aggregate financial attributes of companies within increasing radii to generate statistically significant benchmarks, ensuring relevant data is provided to users through a processor and memory system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional financial benchmarking methods are used relying on advisor knowledge, then peer company identification is possible, but the data reliability and geographical relevance deteriorate
Solution Approach 1:
The patent replaces the manual, knowledge-based mechanical system of financial advisors with an automated computational system that uses algorithms to query, filter, and aggregate company data from multiple sources, thereby improving reliability while reducing dependence on human expertise
Solution Approach 2:
The system performs multiple functions including data collection from diverse sources, geospatial filtering, financial metric comparison, and benchmark generation within a single integrated platform, enabling comprehensive benchmarking without requiring separate manual processes
2Loss of information
If publicly traded company data is used for benchmarking, then financial data availability improves, but geographical relevance and comparability deteriorate
Solution Approach 1:
The patent applies geospatial filtering to ensure that benchmark data comes from companies located in the same or similar geographical regions as the subject company, making the benchmarking data locally relevant and adaptable to regional market conditions while maintaining data availability
3Measurement precision
If data filtering is performed at scale to ensure statistical significance, then benchmark accuracy improves, but computational resources and processing time worsen
Solution Approach 1:
The system segments the data filtering process into multiple stages: initial broad data collection, geospatial filtering, financial metric filtering, and final aggregation. This segmented approach enables statistical significance to be achieved while managing computational resources efficiently through progressive refinement
Data Source
AI summary
Aspects of the present disclosure provide techniques for generating relevant attributes data for benchmark comparison based on geospatial boundaries. According to certain embodiments, based on a geospatial-based query using the coordinates of a company, a first group of companies is found within a first radius of the company. The first group of companies is further queried based on a financial metric within a range of the same metric of the company. If the query results in a statistically significant number of companies in the first group of companies, financial attributes of each of the first group of companies are aggregated to develop a benchmark. If the query results in too few companies, a second geospatial-based query is performed at a second radius greater than the first radius. Further iterations of geospatial-based queries are performed at increasing radii until a statistically significant number of companies is found to develop a benchmark.