IDEAS home Printed from https://ideas.repec.org/a/bhx/ojtijf/v9y2024i5p55-70id2210.html
   My bibliography  Save this article

The Advent of Generative AI and Financial Industry

Author

Listed:
  • Umesh Kumar
  • Bhawna Sinha

Abstract

Purpose: This paper explores the recent literature on Generative AI applications in the financial industry and delineates its role in the future. Methodology: Our paper follows secondary research analyzing current literature on Generative AI in finance. It is one of the essential tools for understanding background information, identifying research problems, and filling the literature gaps. This paper studies how Generative AI has potential financial benefits and risks, providing unique insights into the financial landscape in the coming years. Findings: The findings unveil that Generative AI can become a strategic tool to redefine financial services and operational effectiveness. It can substantially improve the services by reducing costs, bringing efficiency, and enhancing corporate performance. It has the enormous transformative power to revolutionize client product and service offerings, improving risk management assessments and bringing efficiency to operations. However, our study indicates that the financial service industry can get into practices and decisions that are potentially unethical and financial exclusion due to an embedded bias in its algorithm and design of Generative AI technologies. Since Generative AI continues to evolve, its role and effectiveness in decision-making are expected to shape the financial services landscape significantly. Unique Contribution to Theory, Practice, and Policy: Generative AI can be a game changer for the financial industry, fueling digital transformation across industries. The transformative potential of generative AI can optimize operations, revolutionize customer experiences, and drive innovation seamlessly in finance. Our paper suggests how policymakers can foresee the challenges ahead due to the Generative AI in finance services, which is challenging the existing regulatory landscape. To stay ahead in the competition, financial firms must balance data privacy and algorithmic bias and ensure the responsible use of AI.

Suggested Citation

  • Umesh Kumar & Bhawna Sinha, 2024. "The Advent of Generative AI and Financial Industry," International Journal of Finance, CARI Journals Limited, vol. 9(5), pages 55-70.
    • Handle: RePEc:bhx:ojtijf:v:9:y:2024:i:5:p:55-70:id:2210
    as

    Download full text from publisher

    File URL: https://www.carijournals.org/journals/index.php/IJF/article/view/2210/2596
    Download Restriction: no
    ---><---

    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:bhx:ojtijf:v:9:y:2024:i:5:p:55-70:id:2210. 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: Chief Editor (email available below). General contact details of provider: https://www.carijournals.org/journals/index.php/IJF/ .

    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.