Financial Entity De-duplication via Multi-System Data Aggregation
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Solution Overview
Problem
Current financial management systems operate in isolation, preventing the aggregation and correlation of data from personal and business financial management systems, which limits the ability to provide comprehensive financial profiling and modeling, and reveals unknown financial connections between entities.
Innovation Solution
A system and method that aggregates financial data from multiple types of financial management systems, identifies and de-duplicates entities, correlates commercial transactions, and stores the data for analysis and processing, enabling the creation of interconnected entity profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from different types of financial management systems are aggregated and correlated, then the completeness and detail of financial profiles is improved, but the complexity of data integration and entity matching increases
Solution Approach 1:
The patent segments the data integration process into distinct modules: data collection from multiple financial management systems, entity identification and extraction, duplicate entity detection and merging, and profile generation. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while achieving comprehensive data aggregation.
Solution Approach 2:
The patent introduces an intermediary entity resolution layer that acts as a mediator between raw financial data from multiple systems and the final aggregated profiles. This intermediary layer standardizes entity identification, matches entities across different data sources, and resolves duplicates, thereby simplifying the integration of heterogeneous financial data systems.
2Measurement precision
If entity identification and de-duplication processes are performed across multiple data sources, then the accuracy of financial profiles is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary entity identification and extraction from each financial management system before aggregation. By pre-processing data to identify potential entities and their attributes in advance, the system reduces the computational burden during the actual aggregation and matching phase, thereby decreasing overall processing time while maintaining identification accuracy.
Solution Approach 2:
The patent transforms entity identification into a parameter-matching problem by comparing key attributes (names, IDs, timestamps) across data sources. By changing the approach from holistic entity comparison to parameter-based matching, the system achieves accurate entity identification with reduced computational complexity and faster processing.
3Quantity of substance
If comprehensive financial data from multiple systems is collected and stored, then the ability to provide detailed financial analysis is improved, but the data storage and management requirements increase
Solution Approach 1:
The patent merges data from multiple financial management systems into a unified aggregated profile structure. By combining entity information, transaction data, and financial attributes into a single standardized profile format, the system efficiently manages large volumes of data from diverse sources without proportionally increasing storage and management complexity.
Solution Approach 2:
The patent creates a universal aggregated profile structure that can accommodate data from various types of financial management systems (personal finance, business accounting, tax preparation, banking). This multi-functional profile structure enables the system to handle diverse data types and formats uniformly, reducing the complexity of data storage and management while maximizing the utility of collected financial data.
Data Source
AI summary
Financial data is obtained from two or more types of financial management systems and analyzed to obtain potential entities data identifying potential entities and attributes associated with the potential entities. Duplicate potential entity data is then identified and eliminated to generate a master entity list. The financial data is also analyzed to identify potential commercial transaction data and one or more attributes associated with the commercial transaction data, including data indicating commercial transactions and the parties associated with the commercial transactions. The commercial transaction data is then analyzed using the master entity list to match entities listed in the master entity list with the parties associated with the commercial transactions. The matched entities are then substituted for the respectively matched parties associated with the commercial transactions to create a master commercial transaction list. The master entity list and the master commercial transaction list data is then stored.