IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v12y2025i5p148-165.html
   My bibliography  Save this article

The Role of Advanced Anomaly Detection in Transforming Program Management in Government with Scikit-Learn, A Machine Learning Library in Python

Author

Listed:
  • Chiedozie M. Okafor

    (Position/Role: Financial Analyst | Independent Researcher | Certified Information Systems Auditor (CISA) Affiliation (Institution/Department): Independent Researcher & Information Systems Audit and Control Association (ISACA) – Abuja Chapter)

  • Owolabi Ogunse

    (Position/Role: IT Manager Affiliation: Independent Researcher,)

  • Mercy Nneoma Iheke

    (Position/Role: Assistant Manager, Grants Assurance | Independent Researcher Affiliation (Institution/Department): Independent Researcher)

  • Dickson O. Oseghale

    (Position/Role: Budget Analyst Affiliation (Institution/Department): Division of Global HIV & TB, U.S Centers for Disease Control and Prevention,)

  • Imuetinyan Ogiehor

    (Position/Role: Senior Stakeholder Engagement Manager)

  • Ebuka Emmanuel Aniebonam

    (Position/Role: MBA Graduate Student, North Star Mutual School of Business Department of Business, Innovation and Strategy. Southwest Minnesota State University)

Abstract

Government agencies increasingly face challenges in managing programs efficiently, especially in preventing fraud, abuse, and resource waste. Traditional oversight techniques often struggle to detect early signs of problems such as resource misallocation, project delays, and budget overruns. This paper explores how anomaly detection, powered by Scikit-learn, a machine learning library in Python, can improve government program management. Using Isolation Forest, One-Class Support Vector Machine (SVM), and Local Outlier Factor models, we illustrate how advanced anomaly detection can monitor budgets, timelines, and resource utilization. Our findings show that these approaches can enhance program efficiency, enable agile risk management, and support data-driven decisions through a hypothetical example focused on government project data.

Suggested Citation

  • Chiedozie M. Okafor & Owolabi Ogunse & Mercy Nneoma Iheke & Dickson O. Oseghale & Imuetinyan Ogiehor & Ebuka Emmanuel Aniebonam, 2025. "The Role of Advanced Anomaly Detection in Transforming Program Management in Government with Scikit-Learn, A Machine Learning Library in Python," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 12(5), pages 148-165, May.
    • Handle: RePEc:bjc:journl:v:12:y:2025:i:5:p:148-165
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrsi/digital-library/volume-12-issue-5/148-165.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrsi/articles/the-role-of-advanced-anomaly-detection-in-transforming-program-management-in-government-with-scikit-learn-a-machine-learning-library-in-python/
    Download Restriction: no
    ---><---

    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:bjc:journl:v:12:y:2025:i:5:p:148-165. 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: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrsi/ .

    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.