Kris-dq.Ai: transforming risk disclosure analysis through artificial intelligence

Corporate risk disclosure plays a critical role in enhancing transparency, supporting investor decision-making, and strengthening corporate governance. However, existing practices among Malaysian public-listed companies remain inconsistent, often characterised by generic or boilerplate disclosures with limited depth and decision-usefulness. While prior studies have introduced various disclosure indices, most tools remain manual, time-consuming, and difficult to apply in practice. This study introduces KRIS-DQ.ai, an AI-driven web application developed to automate the assessment of Key Risk Disclosure Quality (KRIS-DQ). The system is built upon the KRIS-DQ Index, a structured scoring framework that evaluates disclosure quality across multiple key risk categories using a five-level scale ranging from no disclosure to quantitative disclosure. The application allows users to upload annual reports in PDF format, after which the system processes the document, identifies relevant risk disclosures, and generates both an overall KRIS-DQ score and category-level insights. The innovation lies in transforming a traditionally manual academic framework into an automated, user-friendly tool that improves efficiency, consistency, and accessibility. KRIS-DQ.ai enables faster and more systematic analysis compared to manual content coding. This project contributes by bridging the gap between academic research and practical application. It provides a scalable solution for regulators, investors, researchers, and students to evaluate risk disclosure quality more effectively.