Artificial Intelligence and FinTech Technologies for ESG Data and Analysis

Abstract

Artificial Intelligence (AI) and FinTech-powered ESG screening and analysis solutions have become “strategic enablers” that can address some of the inherent ESG information biases and potentially even ESG rating divergences arising from corporate self-reporting, and annualised, backward looking reporting of information.

However, ESG data and methodology divergence remains a key challenge for the investment community. Information biases are often linked to the pluralism of reporting frameworks, corporate self-disclosure issues and a lack of timely reporting cycles and updates.

“Alternative data” gathering using machine learning and NLP technology is key to retrieve ESG information for “black-box” companies in real time, and can provide a meaningful approach for ESG complexity management. In this paper, we are discussing the implications of regulatory and industry expectations around ESG data and frameworks management, and AI-backed solutions to better manage and align ESG information sources, e.g. for issuer and controversies screening.

Keywords: ESG, UN SDG, Fintech, Artificial Intelligence, Blockchain, XBRL, Tagging, Sentiment Analysis

JEL Classification: F, G