Optimizing ESG Risk Assessment Processes with AI-Driven Process Mining: A Framework for Proactive Sustainability Management

The growing focus on sustainability and mitigation of environmental risks has made the ESG (Environmental, Social and Governance) framework a central element in the strategic and operational management of modern organizations. In a context marked by increasingly stringent regulatory pressures – such as the Corporate Sustainability Reporting Directive (CSRD) and the recommendations of the Task Force on Climate-related Financial Disclosures (TCFD) – companies are now called upon to integrate ESG metrics into their decision-making processes, in order to address global challenges such as climate change, biodiversity loss and the necessary ecological transition. However, the fragmentation of available data and the lack of adequate predictive tools continue to hinder truly effective and future-oriented ESG management. This study proposes an innovative operating model based on the synergistic integration between Artificial Intelligence (AI) and Process Mining, with the aim of improving monitoring, automation and transparency in ESG processes. AI allows the processing of large volumes of heterogeneous and unstructured data, while Process Mining allows you to map and optimize business flows, detecting inefficiencies and ensuring traceability. The model proves to be particularly effective for financial institutions and companies operating in sectors with high climate exposure, providing concrete answers to the needs of regulatory compliance and strategic sustainability. The adoption of this approach makes it possible not only to anticipate environmental risks, but also to strengthen organizational resilience and support the transition to a "sustainability-first" paradigm. The study highlights the transformative potential of AI-driven Process Mining in ESG management, offering scalable and replicable solutions for sustainable innovation that combines efficiency, responsibility and competitiveness in the long term.