Audit Documentation Scoring for Targeted QA Review Allocation
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Solution Overview
Problem
Conventional compliance audits require substantial human resources and are time-consuming and error-prone, especially when dealing with complex organizational policies and multiple authoritative sources, leading to incomplete and inaccurate audit documentation.
Innovation Solution
A method and system for pre-quality assurance (PQE) quality estimation that parses audit documentation to identify control statements and textual comments, generates a numeric score based on individual checks, and assigns specific QA resources and timeframes based on the score to ensure consistent and accurate review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual input by auditors is used for vast set of requirements, then audit conclusions can be generated, but the audit conclusions are incomplete or incompatible resulting in inaccurate and unreliable audit results
Solution Approach 1:
The patent replaces manual mechanical input by auditors with automated electronic data processing systems that parse audit documentation, extract control statements and textual comments, and generate audit conclusions automatically. This substitution eliminates human error and inconsistency while maintaining high productivity through automated processing of vast requirement sets.
Solution Approach 2:
The patent creates structured digital copies of audit documentation that can be systematically parsed and analyzed. By converting unstructured manual inputs into standardized digital formats with extracted control statements and textual comments, the system enables automated processing that ensures completeness and compatibility of audit conclusions without increasing time consumption.
2Productivity
If uniform production schedule is used for QA review, then all audit documentation can be reviewed, but audit documentation of varying quality receives identical review resources resulting in inefficient resource allocation
Solution Approach 1:
The patent applies local quality by differentiating QA review resources based on the specific quality characteristics of each audit documentation. High-quality documentation receives standard review while low-quality documentation receives enhanced review with additional resources and expertise. This localized approach optimizes resource allocation while maintaining consistent quality standards across all documentation.
Solution Approach 2:
The patent introduces dynamic resource allocation where QA review resources and timeframes are adjusted based on the quality score of each audit documentation. The system dynamically assigns review teams with appropriate expertise levels and extends review timeframes for documentation requiring more thorough examination, ensuring both efficiency and reliability.
3Reliability
If extensive human capital is deployed for compliance audit, then comprehensive audit coverage is achieved, but the audit process becomes time consuming and error prone
Solution Approach 1:
The patent segments the audit process into distinct automated components: parsing audit documentation, extracting control statements, identifying textual comments, and generating audit conclusions. This segmentation allows each function to be performed by specialized automated modules, achieving comprehensive coverage through systematic processing while reducing overall time consumption compared to manual execution of all tasks.
Solution Approach 2:
The patent performs preliminary automated processing of audit documentation before human review. By pre-extracting control statements and textual comments, and pre-generating audit conclusions, the system prepares documentation in advance for QA review. This preliminary action reduces the time required for subsequent human review while ensuring comprehensive coverage through automated analysis of all documentation elements.
Data Source
AI summary
Pre-quality assurance (QA) quality estimation (PQE) includes uploading an audit documentation into a memory workspace of an audit data processing system, parsing the audit documentation to identify one or more control statements and one or more textual comments, characterizing the audit documentation according to a set of individual PQE Checks processing a formatting of the audit documentation and a facial appearance of the control statements and textual comments, generating a numeric score from the characterization and assigning the audit documentation for a QA process of specific timeframe and specific rigor in correspondence of the generated score.