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A Tool for Explainable Pension Fund Recommendations using Large Language Models

Published: 08 October 2024 Publication History

Abstract

In this demo, we present a prototype tool designed to help financial advisors recommend private pension funds to investors based on their preferences, offering personalized investment suggestions. The tool leverages Large Language Models (LLMs), which enhance explainability by providing clear and understandable rationales for recommendations and effectively handles both sequential and cold-start scenarios. We outline the design, implementation, and results of a user-based evaluation using real-world data. The evaluation shows a high recommendation acceptance rate among financial advisors, highlighting the tool’s potential to improve decision-making in financial advisory services.

Supplemental Material

MP4 File
Demonstration video of ELMAR - Explainable Language Modeling for Financial Advisor Recommendation for the RecSys 2024 demo track.

References

[1]
Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang, Ziqing Hu, Yanjun Qi, Scott Nickleach, Diego Socolinsky, Srinivasan Sengamedu, and Christos Faloutsos. 2024. Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding – A Survey. arxiv:2402.17944 [cs.CL] https://arxiv.org/abs/2402.17944
[2]
Artem Golubev and Oleg Ryabov. 2020. Transformation of Traditional Financial Companies into FinTech. In Proceedings of the International Scientific Conference - Digital Transformation on Manufacturing, Infrastructure and Service (Saint Petersburg, Russian Federation) (DTMIS ’20). Association for Computing Machinery, New York, NY, USA, Article 17, 7 pages. https://doi.org/10.1145/3446434.3446543
[3]
Andrea Iovine, Fedelucio Narducci, Cataldo Musto, Marco de Gemmis, and Giovanni Semeraro. 2023. Virtual Customer Assistants in finance: From state of the art and practices to design guidelines. Computer Science Review 47 (2023), 100534. https://doi.org/10.1016/j.cosrev.2023.100534
[4]
Paul Kielstra. 2023. Finding value in generative AI for financial services. Tech Report. MIT Technology Reviewe Insights, USA. https://www.technologyreview.com/2023/11/26/1083841/finding-value-in-generative-ai-for-financial-services/Produced in Partnership with UBS Group.
[5]
Vanessa Carolina Molina Medina and Andres Cavelier. 2024. Study: Fintech Ecosystem in Latin America and the Caribbean Exceeds 3,000 Startups. Inter-American Development Bank. Retrieved July 23, 2024 from https://www.iadb.org/en/news/study-fintech-ecosystem-latin-america-and-caribbean-exceeds-3000-startups
[6]
Bilin Shao, Xiaojun Li, and Genqing Bian. 2021. A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph. Expert Systems with Applications 165 (2021), 113764. https://doi.org/10.1016/j.eswa.2020.113764
[7]
John Soldatos and Dimosthenis Kyriazis. 2022. Big Data and Artificial Intelligence in Digital Finance. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-030-94590-9
[8]
Lei Wang and Ee-Peng Lim. 2023. Zero-Shot Next-Item Recommendation using Large Pretrained Language Models. arxiv:2304.03153 [cs.IR]

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cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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New York, NY, United States

Publication History

Published: 08 October 2024

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Author Tags

  1. LLM
  2. Large Language Model
  3. Pension funds
  4. Recommender System
  5. fintech
  6. recsys

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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