Assessing Alignment of Climate Disclosures Using NLP for Financial Markets

Explore a 30-minute conference talk from the Toronto Machine Learning Series (TMLS) that delves into using Natural Language Processing (NLP) for assessing climate-related financial disclosures. Learn how data scientists Quoc Tien Au and Aysha Cotterill from Manifest Climate propose an efficient approach to evaluating climate information in lengthy corporate documents. Discover their innovative model that combines TF-IDF, sentence transformers, and multi-label k-nearest neighbors (kNN) to assess climate disclosure alignment at scale. Gain insights into how this technology can support decision-making in financial markets by providing granular and transparent climate-related information, ultimately aiding companies and stakeholders in reducing environmental impact and climate-induced risk exposure.