Transformer-Based Natural Language Processing for ESG Scoring and Sustainable Finance

The growing integration of Environmental, Social, and Governance (ESG) criteria into investment strategies has highlighted the necessity for more reliable and scalable evaluation mechanisms. Conventional ESG assessments are often constrained by manual processes, fragmented datasets, and difficulties in analyzing large volumes of unstructured information. This study proposes an AI-enabled ESG evaluation framework that addresses these limitations by incorporating state-of-the-art Natural Language Processing (NLP) models, particularly transformer-based architectures, to interpret data from corporate disclosures, media articles, and social platforms. By combining predictive modeling with real-time sentiment analysis, the system produces dynamic insights into ESG performance, thereby supporting evidence-based and sustainable investment decisions. Empirical implementation demonstrates notable reductions in evaluation time, enhanced consistency of ratings, and the ability to process diverse datasets at scale. Collectively, these capabilities position the framework as a competitive tool for investors seeking to align financial returns with ethical and sustainable practices.