Inter-Parameter Correlation Modeling for Data Assurance Scoring
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Solution Overview
Problem
Auditing large data sets with numerous parameters is challenging due to overwhelming volumes and time constraints, leading to reduced accuracy and potential missed errors, especially in environments with stringent requirements like financial auditing, where national and international regulations must be considered.
Innovation Solution
A computer-implemented method using an assurance determination model that establishes inter-parameter correlations, processes historical data sets, calibrates these correlations, and predicts a projected value for an investigated parameter, comparing it with actual values to determine a value assurance score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If sampling is used to audit large data sets, then the auditing process becomes manageable under time constraints, but the accuracy and completeness of error detection deteriorates
Solution Approach 1:
The patent replaces manual auditing mechanisms with an automated computer-implemented assurance determination model. The system automatically establishes inter-parameter correlations, processes historical data sets, calibrates correlations, and generates assurance scores without human intervention, thereby eliminating time constraints while maintaining comprehensive data analysis accuracy.
Solution Approach 2:
The system creates a virtual model of the auditing process that replicates and analyzes complete data sets without physically examining each record. The assurance determination model processes copies of historical data and generates projected values that can be compared against actual values, enabling full-data analysis without the time burden of manual review.
2Measurement precision
If the auditor reviews all parameters in large data sets, then the accuracy of the review improves, but the complexity and time required for the auditing process increases
Solution Approach 1:
The patent segments the auditing process into distinct automated modules: data reception, inter-parameter correlation establishment, historical data processing, correlation calibration, projected value generation, and assurance score determination. Each module handles specific aspects of the analysis independently, reducing overall system complexity while enabling comprehensive parameter review.
Solution Approach 2:
The system dynamically adjusts parameters such as correlation strengths and calibration factors based on historical data analysis. By automatically modifying these parameters through calibration processes, the system adapts to different data sets and auditing requirements without increasing manual complexity, maintaining high accuracy across varying contexts.
3Manufacturing precision
If manual auditing of large data sets is performed, then detailed examination of each parameter is possible, but the productivity and efficiency of the auditing process decreases
Solution Approach 1:
The patent replaces manual auditing mechanics with automated computational processes. The computer-implemented model processes historical data sets and generates assurance scores at speeds impossible for human auditors, thereby dramatically increasing productivity while maintaining thorough examination of all parameters through systematic correlation analysis.
Solution Approach 2:
The system enables continuous automated auditing operations without the interruptions, fatigue, or time constraints that limit human productivity. The assurance determination model can process data sets continuously and scale to handle increasing volumes of records without degradation in examination thoroughness or efficiency.
Data Source
AI summary
Methods for determining a value assurance score for an investigated parameter in an application by means of an assurance determination model, in large data sets, in particular in such application contexts as financial record keeping (auditing). The methods are computer-implemented and involves establishing one or more inter-parameter correlations between at least a subset of the potential parameters, where the correlations link at least two of the parameters of the subset.