Leveraging AI and machine learning for ESG data analysis and sustainable investment decision-making

Abstract

AI and ML may be used to process large amounts of ESG data to assess the sustainability of a company as well as its ability to generate financial returns. We are exploring the disruptive approach to processing ESG data with applications of AI and ML and will focus on building predictive models using ESG factors for both sustainability and investment performance. The data used in this research will be collected from a wide range of public and private sources. Supervised and unsupervised learning based on downsampling, feature scaling and binning methods will be used to process ESG data. We will also investigate the potential to apply various types of ensemble models, which provide a significant improvement in terms of model robustness and accuracy. Additionally, the paper presents case studies illustrating how demonstrable data enable us to explore causality among financial performance and sustainability factors in the sectors where ESG is of paramount importance. The aim of this approach behind digital transformation of ESG data is to help investors extract deeper financial implications for ESG factors, particularly for building long-term financial returns as well as for making more informed and sustainable investment decisions.