IDEAS home Printed from https://ideas.repec.org/a/aza/airwa0/y2025v3i4p350-361.html
   My bibliography  Save this article

Creating talking points for client advisers at banks to promote sustainable investing

Author

Listed:
  • Zi Yi, Ewe

    (Machine Learning Engineer, Gojek, Singapore)

  • Varakantham, Pradeep Reddy

    (School of Computing and Information Systems, Singapore)

  • Megargel, Alan

    (Singapore Management University, Singapore)

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/.

Suggested Citation

  • Zi Yi, Ewe & Varakantham, Pradeep Reddy & Megargel, Alan, 2025. "Creating talking points for client advisers at banks to promote sustainable investing," Journal of AI, Robotics & Workplace Automation, Henry Stewart Publications, vol. 3(4), pages 350-361, April.
  • Handle: RePEc:aza:airwa0:y:2025:v:3:i:4:p:350-361
    as

    Download full text from publisher

    File URL: https://hstalks.com/article/9224/download/
    Download Restriction: Requires a paid subscription for full access.

    File URL: https://hstalks.com/article/9224/
    Download Restriction: Requires a paid subscription for full access.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    machine learning; natural language processing; investment products; client advisers; talking points;
    All these keywords.

    JEL classification:

    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • G2 - Financial Economics - - Financial Institutions and Services

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aza:airwa0:y:2025:v:3:i:4:p:350-361. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Henry Stewart Talks (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.