Automated Compliance Analytics via NLP Entity Extraction
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Solution Overview
Problem
Current systems lack efficient methods for automatically extracting and analyzing compliance-related information from vast amounts of digital data to ensure organizations adhere to regulatory requirements, necessitating manual expertise and time-consuming processes.
Innovation Solution
The implementation of natural language processing (NLP) and machine learning (ML) techniques to automatically identify and extract compliance named entities and profiles from text data, enabling the determination of compliance profiles and matching entities with regulatory guidelines, thus facilitating automated compliance analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual expertise and time-consuming processes are used to ensure compliance, then accuracy and reliability of compliance analysis is improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The patent replaces manual compliance analysis processes with an automated NLP-based system that extracts entities, relationships, and compliance information from unstructured text data. The system uses computational models to perform compliance checking, substituting human expertise with automated algorithms that can process large volumes of data rapidly while maintaining accuracy through structured extraction and validation mechanisms.
Solution Approach 2:
The system enables self-service compliance analysis by automatically extracting compliance information from various data sources, generating compliance profiles, and identifying violations without requiring manual intervention. The automated system performs compliance checking, risk assessment, and reporting functions that traditionally required human analysts, allowing the system to serve itself in the compliance analysis process.
2Productivity
If automated NLP and ML techniques are used to extract compliance information, then productivity and time efficiency is improved, but device complexity and system complexity increases
Solution Approach 1:
The patent segments the compliance analysis system into distinct functional modules: data extraction module, entity recognition module, relationship extraction module, compliance profile generation module, and violation detection module. Each module handles a specific aspect of the compliance analysis process, making the overall complex system more manageable and easier to implement through modular architecture.
Solution Approach 2:
The system introduces intermediate representation layers that bridge raw text data and compliance decisions. Entity graphs and compliance profiles serve as intermediary structures that transform unstructured text into structured compliance information, simplifying the complexity by providing clear intermediate steps between data input and compliance output.
3Loss of information
If vast amounts of digital data are processed to ensure compliance, then completeness of compliance information is improved, but loss of time and processing requirements increase
Solution Approach 1:
The patent extracts only the relevant compliance information from vast amounts of digital data using NLP techniques. The system identifies and extracts entities, relationships, and compliance-relevant text segments, filtering out unnecessary information. This extraction approach maintains information completeness for compliance purposes while significantly reducing the volume of data that requires processing.
Solution Approach 2:
The system performs preliminary data processing and structuring before compliance analysis by pre-extracting entities and relationships from text data and organizing them into compliance profiles. This preliminary action prepares the data in advance, reducing the time required for subsequent compliance checking and ensuring that only processed and structured data is analyzed for compliance violations.
Data Source
AI summary
Embodiments providing analytics on a compliance profile of type organization and a compliance named entity of type organization by a processor. Text data may be extracted from one or more data sources representing one or more objects describing a compliance named entity of type organization expected to perform an obligation. A compliance profile of type organization may be determined for the compliance named entity of type organization according to the extracted text data. One or more compliance named entities of type organization may be matched to the one or more compliance profiles of type organization.