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Creating talking points for client advisers at banks to promote sustainable investing
Journal of AI, Robotics & Workplace Automation,
3
(4),
350-361
(2025)
Abstract
Environmental, social and governance (ESG) factors have become key non-financial factors for investors to evaluate companies with respect to understanding material risks and growth opportunities. While not mandatory, companies are providing ESG reports that outline progress in different ESG metrics (six broad metrics and 15 specific ones). Client advisers (CAs) read these reports to identify key metrics of interest to investors. Given the number of companies and investment products, however, it is not feasible for CAs to read all the reports, which can sometimes run into tens or hundreds of pages). The authors have developed multiple frameworks building on leading approaches in natural language understanding (NLU) to identify relevant talking points in each document and then filter out the most important ones. A large bank has evaluated these approaches on a proprietary dataset of more than 100 sustainability reports and provided an F1 score of over 0.8. The system is currently being evaluated for integration into the bank’s decision-assist framework for client advisers. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: machine learning; natural language processing; investment products; client advisers; talking points
The full article is available to subscribers to the journal.
Author's Biography
Ewe Zi Yi received a Diplôme d’ingénieur (Master of Engineering) from CentraleSupélec (Ecole Centrale Paris) in 2021, as well as a Master (research) and Bachelor of Engineering (industrial and systems engineering) from the National University of Singapore (NUS) in 2021 and 2020 respectively. He is currently working as a Machine Learning Engineer at Gojek, a Southeast Asian on-demand ride-hailing and delivery platform based in Indonesia, where he works with a team to build machine learning (ML) platforms for builders of ML models to easily serve, experiment, monitor and orchestrate ML models and pipelines at production scale by themselves.
Pradeep Reddy Varakantham Dr Pradeep Reddy Varakantham is a Professor in the School of Computing and Information Systems at Singapore Management University. He is a Lee Kuan Yew fellow and directs the CARE AI Lab. His research is at the intersection of artificial intelligence (AI), operations research and machine learning (ML) with specific focus on employing reinforcement learning to solve sequential matching problems. He has published extensively in top-tier conferences and journals in AI. Pradeep and his group have developed systems that have been deployed by multiple government agencies in Singapore in domains of transport and safety/security. Recently, his research has received awards at top conferences in AI/OR (Autonomous Agents and Multiagent Systems [AAMAS], International Conference on Automated Planning and Scheduling [ICAPS], Informs Innovative Applications in Analytics Award [IAAA]), from government agencies in Singapore (Singapore Civil Defence Force) and Google Research (AI for Social Good) grant. Furthermore, he has received multi-million-dollar grants from AI Singapore to investigate trustworthy collaboration between AI agents and humans. Pradeep served as a general chair for ICAPS 2022 and was on the board of directors for International Conference on Autonomous Agents and Multiagent Systems (IFAAMAS) (governing body of AAMAS) until 2022. He serves on the ICAPS council (governing body of ICAPS).
Alan Megargel Dr Alan Megargel (M’19) received a Doctor of Innovation degree from Singapore Management University (SMU) in 2018, and a Master of Science degree in software, systems and information engineering from Sheffield University, UK in 1996. He is currently an Associate Professor of Information Systems (Practice) at SMU, where he serves as the faculty adviser and coordinator for their undergraduate financial technology track/major. Prior to joining SMU, he held various roles in industry including Chief Technology Officer at TIBCO Software Asia, Vice President and Head of Service-oriented Architecture at OCBC Bank, and Senior Enterprise Architect at ANZ Bank. Alan’s current research interests include microservices architecture, digital banking and financial technology. Alan is a member of the Institute of Electrical and Electronics Engineers (IEEE) and Singapore Computer Society (SCS).
