Precedent-Based Document Generation Using AI Similarity Matching
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Solution Overview
Problem
Conventional document generation processes for business transactions are resource-intensive, time-consuming, and prone to inaccuracies, posing challenges to scalability and time to market, especially in generating regulatory-compliant documentation for structured investments.
Innovation Solution
A precedent-dependent document generation tool using natural language processing (NLP) and machine learning models, such as large language models (LLMs), to automate the creation of document formatting and content by leveraging an archive of past deals, employing vector embeddings and cross encoders for similarity matching, and updating document elements based on old-to-new value maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional document automation processes are used, then document generation can be performed, but resource consumption increases and time to market extends
Solution Approach 1:
The system copies and adapts precedent documents from historical deals to generate new documents. By retrieving similar precedent documents and automatically adapting them to new events, the system avoids creating documents from scratch, significantly reducing generation time and resource consumption while maintaining compliance with regulatory requirements
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing precedent documents in a structured format with extracted entities and relationships. This preparation enables rapid retrieval and adaptation of relevant document templates when new events occur, eliminating the need for time-consuming document creation during critical time windows
2Reliability
If conventional document generation methods are used, then documents can be produced, but inaccuracies are introduced
Solution Approach 1:
The system incorporates feedback mechanisms where the adapted precedent document is reviewed and validated against the new event parameters. The feedback loop ensures accuracy by comparing generated document content against regulatory requirements and event specifics, allowing for automatic corrections while maintaining high generation speed through automated validation processes
Solution Approach 2:
The system replaces manual mechanical document generation processes with automated AI-based systems that use natural language processing, entity extraction, and template adaptation algorithms. This substitution eliminates human errors in document creation while maintaining or improving generation speed through automated processing
3Reliability
If manual document review processes are used, then accuracy can be verified, but resource consumption increases
Solution Approach 1:
The system performs self-service verification by automatically validating generated documents against regulatory templates and event parameters. The automated compliance check ensures document accuracy without requiring extensive manual review resources, allowing the system to verify its own output while maintaining high reliability
Data Source
AI summary
Systems and methods for providing an archive of precedents are provided. The archive of precedents includes determining precedent data; parsing a new-event XML file for string data, wherein the string data is related to a new event; generating a vector embedding from the string data; determining a similar embedding in a vector space, wherein the similar embedding is a vector embedding of an instance of precedent data; parsing XML files of the precedent and new event and using parsed data to generate an old-to-new value map; retrieving the corresponding XML file and the corresponding precedent-event document associated with the vector embedding of the instance of precedent data; and providing, as input to a natural language processing model, the list of text strings recovered from the editable form of the document of the precedent event and the old-to-new value map.