Entity Risk Scoring via Universal Data Quality Validation
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Solution Overview
Problem
Current systems in online commerce are inadequate for efficiently evaluating digital information for entity performance and risk scoring due to reliance on Non-Validated Data, leading to inappropriate decision-making in transactions.
Innovation Solution
A method and system for data aggregation that identifies universal data elements, generates Ultimate Data Quality (UDQ) through profile and commercial activity information, and computes performance attribute metrics to produce an overall performance score, such as AxioScore™, using Artificial Intelligence and Big Data Analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Non-Validated Data (NVD) from single sources is used for entity evaluation, then data availability is improved, but measurement precision and reliability of performance scoring deteriorates
Solution Approach 1:
The patent combines multiple data sources including validated data from multiple entities, unvalidated data from various sources, and contextual information to create a comprehensive evaluation framework. This merging of diverse data sources resolves the contradiction by maintaining data availability while improving scoring accuracy through validation and cross-verification
Solution Approach 2:
The patent introduces an intermediary validation layer that processes and verifies data from multiple sources before final scoring. This intermediary mechanism filters and validates NVD through multiple perspectives, ensuring that data availability is maintained while measurement precision is improved through systematic validation
2Measurement precision
If multiple data sources and validation processes are implemented, then measurement precision and reliability are improved, but device complexity and processing time increase
Solution Approach 1:
The patent segments the evaluation system into distinct modules: data collection from multiple sources, validation processing, contextual analysis, and scoring generation. This segmentation manages system complexity by organizing complex validation processes into manageable, independent components that can be processed systematically
Solution Approach 2:
The patent implements dynamic weighting and adaptive processing where the system adjusts validation depth and processing intensity based on data quality, entity type, and risk level. This dynamic approach maintains high measurement precision while reducing unnecessary processing complexity for lower-risk evaluations
3Reliability
If comprehensive data aggregation and validation are performed, then reliability of transaction decisions is improved, but productivity and processing speed decrease
Solution Approach 1:
The patent performs preliminary validation and data quality assessment during data collection phases, preparing and pre-processing information before the actual scoring decision is needed. This preliminary action ensures that when transactions require evaluation, the validation work is already complete, maintaining reliability while improving processing speed
Solution Approach 2:
The patent changes processing parameters dynamically based on risk levels and entity histories, adjusting the depth of validation and data aggregation required. For low-risk, established entities, processing is streamlined for speed; for high-risk or new entities, comprehensive validation is applied, thus balancing reliability with productivity
Data Source
AI summary
A method for data aggregation includes identifying one or more universal data elements. The method further includes receiving profile information for an entity, the entity being associated with the one or more universal data elements. The method further includes receiving commercial activity information and documentation information associated with the entity. The method further includes identifying, validating and generating an Ultimate Data Quality (UDQ) using the one or more universal data elements, the profile information, the commercial activity information, and the documentation information. The method further includes generating performance attribute metrics associated with the entity based on the UDQ and one or more performance factors associated with the entity. The method further includes generating an overall performance score for the entity using the performance attribute metrics.