Tax Data Entity Association via Matching Algorithms
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Solution Overview
Problem
Existing tax return preparation systems face challenges in efficiently associating tax data with the correct tax entity, particularly when the owner of the tax data is unspecified, leading to errors and inefficiencies in data entry and processing.
Innovation Solution
A computerized tax return system that uses matching rules and algorithms to compare tax data with key attributes of tax entities, generating a data ownership record to accurately associate tax data with the appropriate entity, and optimizing data sets by excluding duplicative information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data entry is used to associate tax data with tax entities, then data accuracy can be maintained through user verification, but time consumption and processing efficiency deteriorate
Solution Approach 1:
The system performs self-service by automatically associating tax data with tax entities using matching algorithms that compare tax data attributes with entity attributes, eliminating the need for manual user verification while maintaining association accuracy
Solution Approach 2:
The manual mechanical process of user verification is replaced with an automated electronic matching algorithm that uses attribute comparison and scoring to determine data-entity associations, significantly reducing time consumption
2Measurement precision
If comprehensive matching rules are applied to determine data ownership, then association accuracy is improved, but system complexity increases
Solution Approach 1:
The matching system is segmented into modular components: attribute extraction modules, comparison modules, scoring modules, and selection modules. Each module handles a specific aspect of the matching process, making the overall complex system manageable and maintainable
Solution Approach 2:
The system uses configurable parameters such as matching thresholds, attribute weights, and scoring criteria that can be adjusted to optimize accuracy without permanently increasing system complexity. These parameters allow flexible adaptation to different matching scenarios
3Loss of information
If all tax data is stored without optimization, then data completeness is maintained, but storage efficiency and processing speed deteriorate
Solution Approach 1:
The system extracts only the essential attributes needed for matching and association from the complete tax data, storing these extracted attributes separately. This allows the system to maintain data completeness by keeping the full data while optimizing storage and processing with the extracted key attributes
Solution Approach 2:
Different parts of the data are treated differently: complete tax data is stored for reference and completeness, while optimized attribute sets are stored for rapid matching and processing. This local differentiation of data quality meets both completeness and efficiency requirements
Data Source
AI summary
Systems, methods, and articles of manufacture for associated tax data in a tax data item with a tax entity to which the tax data item pertains. A computerized tax return system is configured to access a tax data item having tax data regarding a tax entity. The system stores tax entity record(s) for one or more tax entities related to a tax return. The system has a set of matching rules for matching tax data from the tax data item with key attributes of the tax entity record(s) for a respective tax topic, and a matching algorithm for utilizing the matching rules to compare utilize the matching rules to compute a matching score. The tax return system determines a matching tax entity using the matching score. The system associates the matching tax entity with the tax data and generates a normalized data set for the tax data item.