ESG Data Collection with Adaptive AI

Francesco Visalli, Antonio Patrizio, Antonio Lanza, Prospero Papaleo, Anupam Nautiyal, Mariella Pupo, Umberto Scilinguo, Ermelinda Oro, Massimo Ruffolo

2023

Abstract

The European Commission defines the sustainable finance as the process of taking Environmental, Social and Governance (ESG) considerations into account when making investment decisions, leading to more long-term investments in sustainable economic activities and projects. Banks, and other financial institutions, are increasingly incorporating data about ESG performances, with particular reference to risks posed by climate change, into their credit and investment portfolios evaluation methods. However, collecting the data related to ESG performances of corporate and businesses is still a difficult task. There exist no single source from which we can extract all the data. Furthermore, most important ESG data is in unstructured format, hence collecting it poses many technological and methodological challenges. In this paper we propose a method that addresses the ESG data collection problem based on AI-based approaches. We also present the implementation of the proposed method and discuss some experiments carried out on real world documents.

Paper Citation

in Harvard Style

Visalli F., Patrizio A., Lanza A., Papaleo P., Nautiyal A., Pupo M., Scilinguo U., Oro E. and Ruffolo M. (2023). ESG Data Collection with Adaptive AI. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 468-475. DOI: 10.5220/0011844500003467

in Bibtex Style

@conference{iceis23,
author={Francesco Visalli and Antonio Patrizio and Antonio Lanza and Prospero Papaleo and Anupam Nautiyal and Mariella Pupo and Umberto Scilinguo and Ermelinda Oro and Massimo Ruffolo},
title={ESG Data Collection with Adaptive AI},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2023},
pages={468-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011844500003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - ESG Data Collection with Adaptive AI
SN - 978-989-758-648-4
AU - Visalli F.
AU - Patrizio A.
AU - Lanza A.
AU - Papaleo P.
AU - Nautiyal A.
AU - Pupo M.
AU - Scilinguo U.
AU - Oro E.
AU - Ruffolo M.
PY - 2023
SP - 468
EP - 475
DO - 10.5220/0011844500003467
PB - SciTePress