AI Regulatory Text Indexing for Compliance Automation
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Solution Overview
Problem
The financial sector faces high costs and inefficiencies in adapting to changing regulatory requirements due to the need for manual research, analysis, and compliance with international and national regulations, leading to significant time and economic burdens.
Innovation Solution
An autonomous system and process for managing and updating regulatory digital textual documents using artificial intelligence and machine learning to extract, recognize, and index regulatory texts, enabling efficient adaptation to regulatory changes by automating the extraction, recognition, and indexing of textual structures and metadata, and providing a system for monitoring and alerting users to updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual research and analysis of regulatory texts is performed by trained staff, then compliance accuracy is maintained, but time consumption and economic costs increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of regulatory research and analysis performed by trained staff with an automated computer-based system using artificial intelligence and natural language processing. The system automatically acquires, processes, and analyzes regulatory texts, extracting compliance requirements without human intervention, thereby maintaining accuracy while dramatically reducing time consumption.
Solution Approach 2:
The system enables self-service by automatically performing compliance analysis without requiring trained staff to manually research and interpret regulatory texts. The computer-based system independently acquires regulatory documents, extracts relevant information, analyzes compliance requirements, and generates reports, making the compliance function self-sufficient and eliminating dependency on human experts for routine analysis.
2Reliability
If manual compliance operations are performed by trained staff, then regulatory analysis quality is maintained, but economic costs increase significantly
Solution Approach 1:
The patent replaces the expensive manual labor of trained compliance staff with an automated computer-based system. The system uses artificial intelligence and natural language processing to perform regulatory analysis at a fraction of the cost of human expertise, while maintaining or improving analysis quality through consistent, error-free automated processing of regulatory texts.
Solution Approach 2:
The system creates digital copies and representations of regulatory texts, structured data models, and compliance requirements that can be repeatedly analyzed without additional cost. Once the regulatory framework is digitally captured and structured, it can be queried and analyzed infinitely times without requiring repeated human intervention, significantly reducing economic costs while maintaining analysis quality.
3Reliability
If comprehensive regulatory text analysis is performed, then compliance coverage is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex regulatory analysis process into distinct modular components: acquisition of regulatory texts, extraction of structured information, analysis of compliance requirements, and generation of reports. Each module handles a specific aspect of the compliance process, making the overall system more manageable and maintainable while achieving comprehensive compliance coverage through the coordinated operation of these specialized segments.
Solution Approach 2:
The system is designed as a universal platform capable of handling multiple types of regulatory texts and compliance requirements through a single integrated architecture. The computer-based system can process different regulatory documents, extract various types of compliance information, and adapt to different analysis needs, reducing system complexity by avoiding the need for separate specialized systems for each compliance scenario.
Data Source
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AI summary
It is provided a procedure for managing and updating regulatory digital textual documents, wherein the documents each define a textual structure including at least one or more textual portions, one or more reference parameters defined by at least one article and/or one paragraph, and one or more metadata, and wherein the process comprises acquiring a plurality of different documents from at least one external database, extracting the textual portions, the reference parameters and the metadata from each of the textual structures, recognising each textual structure by means of a first logic implementing an artificial intelligence based on a machine learning approach of supervised or zero-shot type, performing a multimodal analysis of the textual structure, by means of the first logic, labelling each of the textual portions, the reference parameters and the metadata to validate the extraction phase, identify the reference parameters and/or the metadata by means of a second logic implementing an artificial intelligence to produce a digital representation compliant with the Akoma Ntoso specifications or other standard for encoding normative texts, to index the textual portions by associating to each textual portion a respective reference parameter and/or a respective metadata by producing a plurality of indexed texts, to record separately each indexed text in an internal database accessible by a user, to logically link each indexed text whose reference parameters and/or metadata are mutually correlated within the database.