AI-Driven Process Mining for ESG Risk Assessment in Sustainable Management

The construction sector faces growing challenges in integrating sustainability, risk man-agement, and regulatory compliance, in line with initiatives such as the European Green Deal, the Corporate Sustainability Reporting Directive, and international building stand-ards. Yet, systematic adoption of ESG metrics in decision-making remains limited due to fragmented data, the lack of predictive tools, and reliance on static reporting. This study proposes a digital framework that combines Artificial Intelligence, Process Mining, and Robotic Process Automation to optimize ESG risk assessment in sustainable construction management. The model, formalized through Business Process Model and Notation, in-tegrates Machine Learning for risk classification and weighting, and leverages Web Scraping and Business Intelligence for dynamic data acquisition. The framework enables consistent risk identification, accurate weighting of ESG factors, and clustering of projects into exposure levels, thereby enhancing data quality, predictive accuracy, and decision transparency. From a scientific standpoint, the research contributes an integrated meth-odology that bridges predictive analytics and process management in the context of sus-tainable construction. From a practical perspective, it provides construction firms and project managers with a structured tool to anticipate, classify, and mitigate ESG risks. Overall, the framework lays the groundwork for the sector’s transition toward data-driven and sustainability-first management practices.