IDEAS home Printed from https://ideas.repec.org/a/bdu/oijmrm/v2y2024i1p44-53id2643.html
   My bibliography  Save this article

The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions

Author

Listed:
  • Mary Mwangi

Abstract

Purpose: The aim of the study was to examine the role of machine learning in enhancing risk management strategies in financial institutions. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study revealed that integration of machine learning into risk management strategies within financial institutions has demonstrated significant potential for enhancing decision-making processes and mitigating various risks. The study have consistently shown that machine learning algorithms outperform traditional statistical methods in areas such as credit risk assessment, fraud detection, market risk management, and loan portfolio optimization. These advancements have led to improved accuracy, efficiency, and timeliness in risk assessment, enabling financial institutions to make more informed decisions while reducing losses and enhancing overall performance. Unique Contribution to Theory, Practice and Policy: Modern Portfolio Theory (MPT), Efficient Market Hypothesis (EMH) & Agency Theory may be used to anchor future studies on role of machine learning in enhancing risk management strategies in financial institutions. Invest in building robust data infrastructure and governance frameworks to support the implementation of machine learning models in risk management practices. High-quality data is crucial for training accurate and reliable machine learning algorithms. Establish regulatory guidelines and standards for the responsible use of machine learning in risk management within the financial industry. These guidelines should address issues such as model transparency, fairness, and accountability to ensure ethical and responsible practices.

Suggested Citation

  • Mary Mwangi, 2024. "The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions," International Journal of Modern Risk Management, IPR Journals and Book Publishers, vol. 2(1), pages 44-53.
  • Handle: RePEc:bdu:oijmrm:v:2:y:2024:i:1:p:44-53:id:2643
    as

    Download full text from publisher

    File URL: https://iprjb.org/journals/IJMRM/article/view/2643
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:bdu:oijmrm:v:2:y:2024:i:1:p:44-53:id:2643. 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://iprjb.org/journals/IJMRM/ .

    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.