A Generative AI - Driven Framework for Intelligent Legal Contract Analysis

Abstract

The process of analysing a legal contract is challenging and time-consuming, requiring careful interpretation of legal language, clause identification, and risk assessment. Most existing AI-based legal tools focus on anomaly detection, rule-based clause extraction, and basic contract summarization; however, they often lack contextual awareness and interactive capabilities. These systems provide limited interpretative insights and do not fully leverage the potential of generative intelligence for comprehensive contract analysis. This study presents a Generative AI-driven framework for intelligent legal contract analysis by integrating advanced Natural Language Processing (NLP) techniques with generative AI models. The proposed system performs automatic clause classification, contextual risk scoring with explanations, and AI-generated contract summarization. Additionally, the platform provides simplified legal interpretations to enhance user comprehension and incorporates an interactive AI assistant for dynamic query handling. By combining clause analysis, risk evaluation, and natural language generation, the system improves extraction, interpretability and usability compared to traditional rule-based approaches. This project aims to reduce manual effort, enhance decision-making quality, and enable both legal professionals and non-expert users to analyse contracts efficiently.

Introduction

This paper presents a Generative AI-driven framework for intelligent legal contract analysis designed to simplify and automate the review of complex legal documents. Traditional contract analysis is often time-consuming, error-prone, and dependent on legal expertise, while existing AI tools mainly focus on rule-based clause extraction and lack contextual understanding and interactive capabilities.

The proposed system combines Natural Language Processing (NLP) and Generative AI to perform:

• Automatic clause classification (e.g., confidentiality, liability, payment, termination).
• Contextual risk assessment with explanatory risk scores.
• AI-generated contract summarization using abstractive techniques.
• Simplified legal interpretation that translates complex legal language into plain English.
• An interactive AI assistant for answering contract-related queries.

The methodology follows a modular pipeline consisting of document input, preprocessing, clause segmentation, classification, risk detection, LLM-based simplification, explanation generation, and output presentation. Contracts are processed clause by clause to improve scalability and maintain contextual accuracy. Risk analysis uses a hybrid approach combining machine learning and rule-based indicators such as ambiguous language, excessive liability, and one-sided obligations.

The implementation uses open-source technologies including Python, NLTK, SpaCy, Scikit-learn, Hugging Face Transformers, PyPDF2, and Streamlit. A locally deployed transformer model (such as FLAN-T5) performs contract simplification without requiring paid APIs. The system provides a user-friendly interface featuring side-by-side comparison of original and simplified clauses, risk highlighting, clause navigation, and downloadable reports.

Conclusion

This study presented a Generative AI-driven Intelligent Contract Analysis Framework aimed at improving the readability and accessibility of complex legal documents. By integrating natural language processing techniques and large language model–based simplification, the system transforms dense contractual text into a clearer and structured format while preserving legal meaning and clause hierarchy. Experimental evaluation demonstrated a reduction in linguistic complexity, improved textual clarity, and consistent processing performance across various contract types. The framework maintains structural integrity and scalable performance. Although it does not replace professional legal review, the proposed solution serves as a supportive analytical tool for preliminary contract understanding. Overall, the framework contributes toward enhancing transparency and accessibility in legal document analysis through automated intelligent processing.

References

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Copyright

Copyright © 2026 Mrs. V. Umadevi, Pavithra P, Sandhiya S, Swetha E, Vishnupriya V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.