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 originale | Anglais |
|---|---|
| titre | Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Proceedings |
| rédacteurs en chef | Michael 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 |
| Editeur | Springer Science and Business Media Deutschland GmbH |
| Pages | 325-340 |
| Nombre de pages | 16 |
| ISBN (imprimé) | 9783030937324 |
| Les DOIs | |
| état | Publié - 1 janv. 2021 |
| Evénement | 21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online Durée: 13 sept. 2021 → 17 sept. 2021 |
Série de publications
| Nom | Communications in Computer and Information Science |
|---|---|
| Volume | 1525 CCIS |
| ISSN (imprimé) | 1865-0929 |
| ISSN (Electronique) | 1865-0937 |
Une conférence
| Une conférence | 21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 |
|---|---|
| La ville | Virtual, Online |
| période | 13/09/21 → 17/09/21 |
SDG des Nations Unies
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