Financial Data Validation System for Automatic Error Correction
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Solution Overview
Problem
Financial data reconciliation and integrity validation are challenging due to errors and inconsistencies, which can lead to incorrect data processing and reporting, necessitating a method to automatically identify and correct data errors within financial data sets.
Innovation Solution
A computer-implemented method that automatically organizes financial data into units of work, validates the data against predefined rules, analyzes errors using historical research information, and executes corrective actions to ensure data integrity without introducing further errors or changes in reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic data validation and error correction is implemented, then data processing accuracy is improved, but system complexity increases
Solution Approach 1:
The validation system is divided into separate modular components: data import module, validation rule engine, error detection module, and corrective action executor. Each module handles specific aspects of data validation independently, making the complex system manageable and maintainable while achieving high accuracy through coordinated operation of specialized sub-systems.
Solution Approach 2:
A research database serves as an intermediary component that stores historical error patterns and corrective actions. This intermediary allows the system to learn from past errors and apply proven correction strategies, improving accuracy without requiring complex real-time analysis logic in the main validation engine.
2Reliability
If comprehensive validation rules are applied to all data, then data integrity is improved, but processing time increases
Solution Approach 1:
The system applies validation rules selectively rather than uniformly to all data. Based on error patterns identified in the research database and the specific characteristics of incoming data, the system applies only the necessary validation checks, avoiding unnecessary processing overhead while maintaining data integrity for critical fields.
Solution Approach 2:
The system pre-loads validation rules and historical error patterns into the research database before processing incoming data. This preliminary preparation allows the validation engine to quickly reference pre-analyzed error patterns and apply appropriate corrections without performing complex real-time analysis, thus maintaining data integrity while reducing processing time.
3Measurement precision
If manual review of deficient units of work is required, then error detection accuracy is improved, but productivity decreases
Solution Approach 1:
The system automatically detects errors using validation rules and independently executes corrective actions based on patterns from the research database. This self-service capability handles routine error detection and correction without human intervention, maintaining high accuracy for common error types while preserving productivity by eliminating manual review for standardized issues.
Solution Approach 2:
The research database accumulates feedback from historical error patterns and corrective actions. This feedback mechanism allows the system to continuously improve its error detection and correction capabilities, achieving high accuracy comparable to manual review while maintaining automated processing speeds. The feedback loop enables the system to learn from past successes and refine its validation strategies.
Data Source
AI summary
In some embodiments, the instant invention includes a computer-implemented method that includes: specifically programming at least one computer system to perform: automatically obtaining input financial data; automatically determining units of work that the input financial data can be organized into; automatically importing the input financial data into a database based on the units of work; automatically validating the imported input financial data, by: identifying deficient units of work based on predefined rules, where each deficient unit of work has a current data error; analyzing, based on research information, the current data error to determine a current corrective action to remedy the current data error; and verifying that the current corrective action does not result in: an additional deficient unit of work, an additional data error, and a change in at least one predefined data report; and automatically executing the current corrective action to remedy the current data error.