Multi-level Tax Data Validation Engine Root Cause Analysis
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Solution Overview
Problem
Complex multi-level tax data validation engines often fail to determine the root cause of errors due to their complexity, leading to difficulties in identifying and correcting data issues, which can result in erroneous data being sent to agencies or clients, and requiring tedious and time-consuming client support processes.
Innovation Solution
A computer-implemented method that selects a gate for data validation among multiple gates with pre-defined validation rules, performs machine learning analysis to determine the applicability of other rules, and generates explicit notifications for data failures, allowing for the identification of the exact cause of errors and optimization of data validation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a multi-level tax data validation engine with many layers of information is used, then data validation comprehensiveness is improved, but the ability to determine root causes of errors deteriorates
Solution Approach 1:
The validation engine is divided into multiple gates, each responsible for specific validation rules at different levels. This segmentation allows the system to maintain comprehensive validation while isolating error sources to specific gates, making root cause determination feasible despite overall system complexity.
Solution Approach 2:
Machine learning analysis acts as an intermediary between the complex validation engine and error diagnosis. The ML component analyzes validation operations and determines applicability of rules, bridging the gap between comprehensive multi-level validation and actionable error root cause identification.
2Manufacturing precision
If comprehensive multi-level validation rules are applied, then data accuracy is improved, but time required for validation increases
Solution Approach 1:
Validation rules are pre-organized into gates with pre-selected organizations of rules at different levels. This preliminary structuring allows the system to apply appropriate validation depth based on data characteristics, maintaining accuracy while reducing unnecessary validation time for straightforward cases.
Solution Approach 2:
The system applies machine learning analysis to determine the applicability of rules in different gates, allowing partial application of comprehensive validation rules based on actual data needs. This prevents excessive validation time while maintaining necessary accuracy through targeted rule application.
3Measurement precision
If detailed error analysis is performed in a complex validation system, then error root cause identification is improved, but system complexity increases
Solution Approach 1:
Machine learning analysis serves as an intermediary that automatically performs detailed error analysis across the multi-level validation gates. The ML component handles the complexity of tracing errors through multiple validation layers, providing precise root cause identification without requiring manual complexity management.
Solution Approach 2:
The system performs machine learning analysis on validation operations to generate feedback about rule applicability and error causes. This automated feedback loop enables detailed error analysis by systematically examining validation failures and tracing them to specific gates and rules, improving root cause identification while managing complexity through automation.
Data Source
AI summary
A computer-method for improving operation of a computer by validating incoming data provided to a tax evaluation software program. The computer-implemented method includes selecting a gate at which the data is to be validated. Selecting is performed from among a plurality of gates. Each of the plurality of gates comprises a corresponding pre-selected organization of validation rules which at least specifies a corresponding level of validation. The gate has a particular organization of validation rules at a particular level of validation. The computer-implemented method also includes validating the data at the gate. The computer-implemented method also includes performing machine learning analysis on the operation of validating the data to determine an applicability of other rules in other gates. The computer-implemented method also includes, responsive to detecting a failure in validation of the data at the gate, generating, and displaying on a display device, a cause for the failure.