IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-05100909.html
   My bibliography  Save this paper

Integrating Risk Management in Fintech and Traditional Financial Institutions through AI and Machine Learning

Author

Listed:
  • Bibitayo Ebunlomo Abikoye

    (Cornell University, SC Johnson Business School, Ithaca, NY, USA.)

  • Wunmi Adelusi

    (Banking Supervision Department, Central Bank of Nigeria, Abuja, Nigeria.)

  • Stanley Chidozie Umeorah

    (University of Michigan, Stephen M. Ross School of Business, Ann Arbor, MI, USA.)

  • Adesola Oluwatosin Adelaja

    (University of Virginia Darden School of Business, Charlottesville, VA, USA.)

  • Cedrick Agorbia-Atta

    (Indiana University, Kelley School of Business, Bloomington, IN, USA.)

Abstract

The rapid evolution of financial technology (fintech) has significantly transformed the financial services landscape, creating opportunities for innovation and introducing new risks. Traditional financial institutions and fintech companies operate under different paradigms, resulting in disparate risk management practices. This paper proposes a comprehensive framework for integrating operations and risk management practices between traditional financial institutions and fintech companies. By leveraging advanced technologies such as artificial intelligence (AI) and machine learning (ML), the framework aims to ensure consistent and effective risk assessment across the financial sector. The financial services industry is characterized by rapid innovation, primarily driven by fintech companies offering various services that enhance efficiency, accessibility, and customer satisfaction. However, the growth of fintech brings substantial risks, including cyber threats, data privacy concerns, regulatory compliance challenges, and operational vulnerabilities. Traditional financial institutions prioritize stability, security, and compliance within established risk management frameworks. The divergence in operational models and risk management approaches creates a fragmented risk landscape, posing significant challenges to the financial system's stability and security. This paper identifies the critical need for a unified framework integrating the risk management practices of traditional financial institutions and fintech companies. The proposed framework leverages AI and ML to enhance the accuracy and comprehensiveness of risk assessments, utilizing a centralized data repository for real-time risk assessment. Unified risk management policies covering cybersecurity, operational risk, regulatory compliance, financial crime, and real-time monitoring and reporting tools ensure robust risk management protocols and prompt response to potential risks. Aligning with regulatory requirements and incorporating best practices from both sectors, the integrated risk management approach enhances the financial ecosystem's stability, security, and public confidence.

Suggested Citation

  • Bibitayo Ebunlomo Abikoye & Wunmi Adelusi & Stanley Chidozie Umeorah & Adesola Oluwatosin Adelaja & Cedrick Agorbia-Atta, 2024. "Integrating Risk Management in Fintech and Traditional Financial Institutions through AI and Machine Learning," Post-Print hal-05100909, HAL.
  • Handle: RePEc:hal:journl:hal-05100909
    DOI: 10.9734/jemt/2024/v30i81236
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    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:hal:journl:hal-05100909. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.