NLP and Computer Vision for Unstructured Audit Report Normalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in efficiently extracting and analyzing information from unstructured and semi-structured Service and Organizational Control (SOC) audit reports, which are crucial for verifying compliance with best practices and assessing risks in outsourcing business functions.
Innovation Solution
The solution involves a computer-implemented method that accesses electronic SOC audit reports, converts the audit information from multiple electronic formats into a normalized format using natural language processing and computer vision, and stores it in a searchable repository. This allows for the extraction of information from unstructured and semi-structured reports, enabling comprehensive analysis and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction and analysis methods are used for unstructured SOC audit reports, then information accuracy can be maintained through human review, but processing time and labor costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical extraction processes with an automated system combining optical character recognition (OCR) technology and natural language processing (NLP) algorithms. The OCR component converts scanned documents and images into machine-readable text, while NLP algorithms automatically extract, classify, and analyze audit information, eliminating the need for manual reading and transcription while maintaining high accuracy through structured data validation rules.
Solution Approach 2:
The system enables self-service extraction where the audit report data automatically populates compliance assessment forms and risk evaluation templates without human intervention. The standardized data structures and pre-configured extraction rules allow the system to autonomously identify, extract, and organize relevant information from unstructured reports into usable formats for compliance decision-making.
2Productivity
If automated extraction systems are implemented without standardization, then processing speed increases, but data consistency and reliability deteriorate across different report formats
Solution Approach 1:
The patent transforms unstructured audit report data into structured formats by applying standardized data schemas and classification taxonomies. The system defines specific parameter structures for different audit report types, converting varied input formats into uniform data models with consistent field names, data types, and validation rules, thereby ensuring reliability while maintaining automated processing capabilities.
Solution Approach 2:
The system creates a universal extraction framework that handles multiple SOC audit report formats (SOC 1, SOC 2, SOC 3) through a single standardized interface. The standardized data structures serve as a common language that can process and normalize information from different report types and sources, ensuring consistent data quality across diverse inputs while enabling scalable automation.
3Reliability
If comprehensive analysis of all audit report details is performed, then complete risk assessment is achieved, but system complexity and computational resources increase
Solution Approach 1:
The patent divides the complex audit report analysis into modular extraction components, each responsible for specific sections or types of information (e.g., control effectiveness, compliance gaps, risk factors). This segmentation allows the system to process different aspects of audit reports independently using specialized algorithms, reducing overall system complexity while maintaining comprehensive coverage through orchestrated integration of modular components.
Solution Approach 2:
The system implements selective extraction that focuses on critical risk-related information rather than processing every detail equally. The NLP algorithms identify and extract only the most relevant audit findings, control deficiencies, and risk indicators based on pre-defined importance criteria, achieving sufficient risk assessment completeness without the computational overhead of analyzing every single data point in detail.
Data Source
AI summary
Embodiments of the invention provide a computer-implemented method that includes accessing an electronic audit report that includes audit information of operations of an entity-under-evaluation (EUA) covering a first time-range, wherein the audit information of the electronic audit report includes multiple electronic format types. A normalized electronic audit report is created by converting the audit information of the electronic audit report from the multiple electronic format types to a normalized electronic format. The normalized electronic audit report is stored in a searchable repository that includes a plurality of normalized electronic audit reports of the EUA covering a plurality of time-ranges that are prior to the first time-range. The normalized electronic format is used to search the searchable repository to determine how stored audit information associated with the EUA and the normalized electronic format has changed over the plurality of time-ranges and the first time-range.