Entity Risk Assessment via Sector Benchmarking
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Solution Overview
Problem
Existing databases lack effective methods to leverage data exchange records to identify relevant information and manage risk levels for entities, such as businesses or individuals, which hinders informed decision-making and relationship cultivation.
Innovation Solution
A computing device and method that analyze data exchange records to determine an entity's sector, calculate a benchmark index, and rank entities based on their risk levels, allowing for informed decisions and relationship management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data exchange records are analyzed to determine entity risk levels, then informed decision-making and relationship cultivation are enabled, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the risk assessment process into distinct functional modules: data collection module, data processing module, risk calculation module, and reporting module. Each module handles specific aspects of the analysis, making the overall complex system manageable and maintainable while achieving comprehensive risk assessment
Solution Approach 2:
The patent introduces intermediary components such as data preprocessing layers that transform raw data into structured formats, and intermediate calculation layers that compute risk scores based on multiple factors. These intermediaries simplify the relationship between raw data and final risk determinations, reducing the apparent complexity of the system
2Measurement precision
If comprehensive data exchange records are stored and analyzed, then risk assessment accuracy improves, but data storage requirements and processing time increase
Solution Approach 1:
The system extracts only the most relevant features and attributes from comprehensive data exchange records for risk assessment. Instead of storing and processing all raw data, the patent identifies and extracts key indicators such as transaction patterns, entity relationships, and behavioral metrics, thereby maintaining measurement precision while reducing storage requirements
Solution Approach 2:
The patent applies partial action by focusing analysis on a subset of most critical data elements rather than processing the entire dataset. The risk calculation model uses weighted contributions from selected data points, achieving sufficient precision without the computational burden of exhaustive data processing
3Reliability
If multiple factors are considered in risk calculation, then risk determination accuracy improves, but calculation complexity and time requirements increase
Solution Approach 1:
The system dynamically adjusts calculation parameters such as the number of factors considered, weighting coefficients, and analysis depth based on the specific context and risk level being assessed. This allows the system to maintain high reliability for critical assessments while reducing computation time for routine evaluations, effectively managing the trade-off between accuracy and speed
Data Source
AI summary
Some aspects provide: analyzing data exchange database records of a first entity; determining a sector with which the first entity is associated by at least one of the analyzing and first entity input identifying the sector; analyzing data exchange database records of second entity(ies) different from the first entity to determine which involve other entities associated with the sector; analyzing the records of the other entities associated with the sector; determining a benchmark index for the sector and an index for the first entity based on factor(s) of the other entities and the first entity; comparing the first entity index to the sector benchmark index and ranking the first entity against the other entities based on the comparing; determining a level of risk for the first entity based on the rank; and determining an amount of a contribution to the first entity based on the level of risk.