Generative AI for Simplified ESG Reporting in Financial Services

Bowden, James and Cummins, Mark and Zhang, Hao (2024) Generative AI for Simplified ESG Reporting in Financial Services. University of Strathclyde, Glasgow.

Full text not available in this repository.Request a copy

Abstract

We evaluate the potential for Generative AI to simplify Environmental, Social, and Governance (ESG) reporting in financial services. Banking and financial institutions are required to comply with ever more stringent and demanding ESG related compliance requirements. A lack of mandatory, universally enforceable sustainable finance standards and guidelines makes effective ESG reporting across countries and industries difficult for financial institutions. Vast amounts of data processing are required, spanning structured quantitative numerical data and unstructured qualitative textual data. Generative AI has the potential to deliver an innovative solution to this ESG reporting challenge. Generative AI offers a number of identifiable capabilities in terms of decision support, including document summarisation; data visualisation; individual and multiple company analytics; and customised report generation. Furthermore, several technical features allow organisations to customise Generative AI systems to meet bespoke business requirements and information technology constraints. These technical features include response speed and agility; multiple versions and algorithmic support; user friendly interface; and scalability and upgradability. In the use case demonstration, we show how a localised large language model can be used to generate responses to a set of common analyst questions pertaining to ESG. This use case brings to life the potential for Generative AI in simplifying compliance in respect of ESG reporting.

CORE (COnnecting REpositories)