IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/dwspv_v1.html
   My bibliography  Save this paper

EAGLE: A Multi-Agent Generative AI System for Personalized Banking Recommendation and Risk-Aware Financial Planning

Author

Listed:
  • Srivastava, Varad

Abstract

Traditional banking, which relies on relationship managers to provide personalized financial advice, recommend products and assess risk, has been left relatively untouched from potential enhancements of Generative AI. In this work, we propose - EAGLE, a multi-agent system for this task, which automates banking recommendations while incorporating real-time, risk-aware financial planning and augments and enhances operations by cutting down on time taken for research on customer profiles, products, financial plans as well as call handling. Through simulated experiments and novel proposed metrics, we establish robust performance on this task using our framework. Our proposed multi-agent system enhances personalization of products recommendation, enables risk-aware financial planning and asset allocation, as well as establishes a foundation for next-generation banking systems.

Suggested Citation

  • Srivastava, Varad, 2025. "EAGLE: A Multi-Agent Generative AI System for Personalized Banking Recommendation and Risk-Aware Financial Planning," OSF Preprints dwspv_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:dwspv_v1
    DOI: 10.31219/osf.io/dwspv_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/680cbbbb8646be16ca25afc4/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/dwspv_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yichen Luo & Yebo Feng & Jiahua Xu & Paolo Tasca & Yang Liu, 2025. "LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management," Papers 2501.00826, arXiv.org, revised Jan 2025.
    2. Yangyang Yu & Haohang Li & Zhi Chen & Yuechen Jiang & Yang Li & Denghui Zhang & Rong Liu & Jordan W. Suchow & Khaldoun Khashanah, 2023. "FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design," Papers 2311.13743, arXiv.org, revised Dec 2023.
    3. Tianyu Zhou & Pinqiao Wang & Yilin Wu & Hongyang Yang, 2024. "FinRobot: AI Agent for Equity Research and Valuation with Large Language Models," Papers 2411.08804, arXiv.org.
    4. Taejin Park, 2024. "Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework," Papers 2403.19735, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tao Ren & Ruihan Zhou & Jinyang Jiang & Jiafeng Liang & Qinghao Wang & Yijie Peng, 2024. "RiskMiner: Discovering Formulaic Alphas via Risk Seeking Monte Carlo Tree Search," Papers 2402.07080, arXiv.org, revised Feb 2024.
    2. Wentao Zhang & Lingxuan Zhao & Haochong Xia & Shuo Sun & Jiaze Sun & Molei Qin & Xinyi Li & Yuqing Zhao & Yilei Zhao & Xinyu Cai & Longtao Zheng & Xinrun Wang & Bo An, 2024. "A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist," Papers 2402.18485, arXiv.org, revised Jun 2024.
    3. Vasant Dhar & Jo~ao Sedoc, 2025. "DBOT: Artificial Intelligence for Systematic Long-Term Investing," Papers 2504.05639, arXiv.org.
    4. Yupeng Cao & Zhi Chen & Prashant Kumar & Qingyun Pei & Yangyang Yu & Haohang Li & Fabrizio Dimino & Lorenzo Ausiello & K. P. Subbalakshmi & Papa Momar Ndiaye, 2024. "RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data," Papers 2404.07452, arXiv.org, revised May 2025.
    5. Shijie Han & Jingshu Zhang & Yiqing Shen & Kaiyuan Yan & Hongguang Li, 2025. "FinSphere, a Real-Time Stock Analysis Agent Powered by Instruction-Tuned LLMs and Domain Tools," Papers 2501.12399, arXiv.org, revised Jul 2025.
    6. Weixian Waylon Li & Hyeonjun Kim & Mihai Cucuringu & Tiejun Ma, 2025. "Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?," Papers 2505.07078, arXiv.org, revised May 2025.
    7. Xiangyu Li & Yawen Zeng & Xiaofen Xing & Jin Xu & Xiangmin Xu, 2025. "HedgeAgents: A Balanced-aware Multi-agent Financial Trading System," Papers 2502.13165, arXiv.org.
    8. Han Ding & Yinheng Li & Junhao Wang & Hang Chen, 2024. "Large Language Model Agent in Financial Trading: A Survey," Papers 2408.06361, arXiv.org.
    9. Herbert Dawid & Philipp Harting & Hankui Wang & Zhongli Wang & Jiachen Yi, 2025. "Agentic Workflows for Economic Research: Design and Implementation," Papers 2504.09736, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:osf:osfxxx:dwspv_v1. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.