In the modern legal and corporate environment, contract review remains a critical yet resourceintensive task, often requiring significant time, expertise, and manual effort. Traditional approaches to reviewing legal documents are prone to human error, inconsistencies, and inefficiencies, especially when dealing with large volumes of contracts. This paper presents ContractRisk Analyzer AI, an intelligent system designed to automate the identification and classification of legal risk within contract documents using Natural Language Processing (NLP) techniques. The proposed system processes legal contracts in both PDF and plain-text formats, extracting and segmenting the content into individual clauses using advanced sentence tokenization methods. Each clause is evaluated against a structured legal risk knowledge base categorized into High, Medium, and Low risk levels. The classification process is driven by a rule-based NLP engine that ensures transparency by identifying the exact phrases responsible for risk detection. A weighted scoring mechanism aggregates clause-level risks to generate an overall document risk percentage, providing a clear and interpretable assessment. Furthermore, the system integrates an intelligent recommendation module that offers actionable insights based on detected risks, assisting users in making informed legal decisions. Implemented as a web-based application using the Flask framework, the system provides an intuitive user interface with interactive visualizations, including a dynamic risk gauge and clause-level analysis tables. The proposed solution demonstrates the effectiveness of explainable AI in the legal domain, offering a scalable, accessible, and efficient alternative to traditional contract review processes. It significantly reduces review time while maintaining analytical accuracy, making it valuable for legal professionals, organizations, and academic users.
Anusha et al. (Thu,) studied this question.