Adaptive Entity Screening with Dynamic Match Scoring
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Solution Overview
Problem
Existing entity screening systems lack transparency in their matching processes and do not adjust for updated information, leading to static match scores that do not reflect changes in entity data over time.
Innovation Solution
A system for adaptive and transparent entity screening that uses computer processors and software instructions to access entity lists, apply matching rules, and generate updated pair match scores based on changes in entity information, providing users with a user interface to view attribute match scores and adjust weightings for more accurate and dynamic risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing entity screening systems use static matching rules, then the system complexity is reduced, but the match scores become outdated and do not reflect changes in entity data over time
Solution Approach 1:
The patent implements dynamic match scoring by continuously updating entity information and reapplying matching rules to generate new match scores. The system transitions from static, one-time matching to a dynamic process where match scores automatically adjust as entity data changes, ensuring scores reflect current entity states without requiring manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring changes in entity information and using this feedback to trigger automatic rematching. When entity data is updated, the system detects the change, reapplys matching rules, and adjusts match scores accordingly, creating a closed-loop system that continuously improves matching accuracy based on new information.
2Loss of information
If existing entity screening systems hide matching logic, then the system is easier to operate, but transparency into the screening process is lost
Solution Approach 1:
The patent introduces an intermediary layer that bridges the gap between complex matching logic and user understanding. This layer provides detailed explanations of how match scores are calculated, what attributes contribute to matching, and why entities are matched or not matched, making the system transparent without requiring users to understand the underlying complex algorithms.
Solution Approach 2:
The system segments the matching process into distinct, explainable components such as individual attribute comparisons (name, address, phone number) and their respective weightings. By breaking down the overall match score into contributable parts, the system maintains ease of operation while providing transparent insights into how each attribute influences the final matching result.
3Reliability
If entity screening is performed only once, then the process is faster, but the results do not account for updated entity information over time
Solution Approach 1:
The patent implements periodic action by scheduling automatic rematching at defined intervals or triggered by specific events such as entity data updates. This approach ensures that match scores are refreshed periodically to reflect current entity information while avoiding continuous, resource-intensive processing, thus balancing reliability with time efficiency.
Solution Approach 2:
The system performs self-service by automatically detecting changes in entity information and initiating rematching without requiring manual triggers. This autonomous operation ensures that risk assessments remain accurate and up-to-date while minimizing the time and human effort required to maintain reliability, as the system handles updates autonomously.
Data Source
AI summary
Systems and methods for adaptive and transparent entity screening are provided. In an aspect, a first entity list comprising a plurality of first entity records is accessed. The first entity records comprise a plurality of first entity identifying attributes. A second entity list comprising a plurality of second entity records is also accessed, wherein the second entity records comprise a plurality of second entity identifying attributes. In certain aspects, an entity screening model pairs the first entity identifying attributes of the first entity record with the second entity identifying attributes of the second entity record, executes different matching algorithms on pairs of entity identifying attributes, and determines an overall likelihood that the pair of entity records are a match based on aggregation of match scores from the plurality of matching algorithms.