Text Categorization System for Regulatory Compliance Automation
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Solution Overview
Problem
Highly-regulated industries face inefficiencies in processing and organizing large volumes of regulatory information for compliance, requiring significant resources and manual effort from IT and risk compliance professionals.
Innovation Solution
Implementing a text categorization process using machine learning and artificial intelligence to automate the identification, categorization, and organization of regulatory text, enabling efficient response to regulatory assessments and thematic organization of regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual and automated processes are used for filtering and organizing regulatory information, then compliance requirements can be met, but resource intensity and processing time increase significantly
Solution Approach 1:
The patent replaces manual mechanical processes (human professionals filtering and organizing regulatory information) with an automated text categorization system using machine learning and artificial intelligence. This substitution maintains compliance accuracy while dramatically improving processing efficiency by automating the identification, categorization, and organization of regulatory text from large volumes of information.
Solution Approach 2:
The system enables self-service by automatically performing text categorization and regulatory information filtering without requiring human intervention for each processing task. The machine learning model autonomously identifies, categorizes, and organizes regulatory text, allowing the system to serve itself in processing compliance information while reducing dependency on skilled IT and risk compliance professionals.
2Measurement precision
If skilled professionals manually process regulatory assessments, then accurate compliance determination is achieved, but time consumption and resource requirements increase
Solution Approach 1:
The system performs preliminary action by pre-processing and categorizing regulatory text in advance using machine learning models. The text categorization process prepares regulatory information beforehand, organizing it into structured categories that can be quickly retrieved and analyzed, thereby reducing the time required for skilled professionals to determine regulatory applicability while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary text categorization system that acts as a mediator between raw regulatory information and human analysts. The AI-based categorization system processes and structures regulatory text, presenting organized results to professionals who then make final compliance determinations. This intermediary layer reduces the time professionals spend on initial information gathering while preserving measurement precision.
3Reliability
If volumes of regulatory rules and laws are analyzed manually, then comprehensive compliance coverage is achieved, but resource intensity increases
Solution Approach 1:
The patent applies segmentation by dividing the large volume of regulatory information into smaller, manageable text segments that can be independently categorized and processed. The machine learning system processes regulatory rules and laws in discrete units, organizing them into structured categories. This segmentation enables comprehensive compliance coverage while reducing system resource requirements compared to processing entire regulatory documents as single units.
Solution Approach 2:
The system changes parameters by transforming unstructured regulatory text into structured categorical data with defined attributes and relationships. The text categorization process converts raw regulatory information into organized datasets with specific parameters (categories, tags, hierarchical structures), enabling efficient processing and analysis while maintaining comprehensive compliance coverage with reduced resource intensity.
Data Source
AI summary
Systems, methods, apparatuses, and computer-readable media for categorized text determination and organization are described. In one embodiment, an apparatus may include a processor and a memory storing instructions which when executed by the processor cause the processor to determine a plurality of contextual text elements in at least one text source, combine the plurality of contextual text elements, classify events associated with at least a portion of the plurality of contextual text elements, and determine text elements related to at least a portion of the contextual text elements. Other embodiments are described.