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Automated ESG Report Analysis by Joint Entity and Relation Extraction

  • Crédit Agricole Assurances

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Résumé

The banking industry has lately been under pressure, notably from regulators and NGOs, to report various Environmental, Societal and Governance (ESG) metrics (e.g., the carbon footprint of loans). For years at Crédit Agricole, a specialized division examined ESG and Corporate Social Responsibility (CSR) reports to ensure, e.g., the bank’s commitment to de-fund coal activities, and companies with social or environmental issues. With both an intensification of the aforementioned exterior pressure, and of the number of companies making such reports publicly available, the tedious process of going through each report has become unsustainable. In this work, we present two adaptations of previously published models for joint entity and relation extraction. We train them on a private dataset consisting in ESG and CSR reports annotated internally at Crédit Agricole. We show that we are able to effectively detect entities such as coal activities and environmental or social issues, as well as relations between these entities, thus enabling the financial industry to quickly grasp the creditworthiness of clients and prospects w.r.t. ESG criteria. The resulting model is provided at https://github.com/adimajo/renard_joint.

langue originaleAnglais
titreMachine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Proceedings
rédacteurs en chefMichael Kamp, Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastian Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita Ribeiro, João Gama, Ricard Gavaldà, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça
EditeurSpringer Science and Business Media Deutschland GmbH
Pages325-340
Nombre de pages16
ISBN (imprimé)9783030937324
Les DOIs
étatPublié - 1 janv. 2021
Evénement21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online
Durée: 13 sept. 202117 sept. 2021

Série de publications

NomCommunications in Computer and Information Science
Volume1525 CCIS
ISSN (imprimé)1865-0929
ISSN (Electronique)1865-0937

Une conférence

Une conférence21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
La villeVirtual, Online
période13/09/2117/09/21

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

  1. SDG 12 - Consommation et production responsables
    SDG 12 Consommation et production responsables

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