AI-Powered Contract Analysis System Using OCR, NLP, and Large Language Models
Abstract
The process of manually analyzing contracts often takes long amounts of time, requires legal training and knowledge, and is not typically affordable to individuals and small businesses. In addition, current forms of analysis offer only moderately compliant output due to the fact that they typically only provide basic functionality of reviewing documents and the ability to search for key words, and therefore do not provide the ability to intelligently assess and identify potential risk, nor do they provide multi-lingual support. In an effort to improve the process of contract analysis, this paper introduces an AI-based Contract Analysis System that has been developed to facilitate and automate the process of interpreting legal documents. Included in this system is Optical Character Recognition (OCR) for the extraction of text from PDF and image formats, Natural Language Processing (NLP) for the identification of clauses and semantic analysis of those clauses, and Large Language Models (LLM) for creating short summaries and contextual explanations of clauses. This planned framework is intended to provide a clause-by-clause analysis of important contract clauses as well as to identify any possible risks, which include the penalty of termination, termination conditions, and compliance obligations. A compliance scoring mechanism is also created to allow for the evaluation of the user's level of compliance with the terms of these contracts. Additionally, the system offers services in various Indian languages to allow users to interpret their legal documents in their chosen language. Tests and evaluations conducted show that the system greatly enhances the effectiveness, accuracy, and usability of legal document analysis when compared to the prior art of manually conducting the same types of analyses. The combination of OCR, NLP, and generative AI technologies enable the solution to support an intelligent and scalable method for performing legal document analyses, and thereby provide users with a method for making decisions with much less complexity and risk.