A CI/CD-Integrated Model for Machine Learning Deployment in Revenue Risk Prevention

Authors

  • Babawale Patrick Okare  Ceridian (Dayforce) Toronto, Canada
  • Tope David Aduloju  Toju Africa, Nigeria
  • Olanrewaju Oluwaseun Ajayi  University of the Cumberlands, Williamsburg, Kentucky, USA
  • Okeoma Onunka  Nigerian Institute of Leather and Science Technology Zaria, Kaduna, Nigeria
  • Linda Azah  Vodacom Business Nigeria [ISP], Ikoyi, Lagos, Nigeria

Keywords:

Continuous Integration and Continuous Deployment, Machine Learning Deployment, Revenue Risk Prevention, DevOps Automation, Model Governance, Financial Analytics

Abstract

The increasing complexity and scale of revenue risk, encompassing fraud, pricing errors, and revenue leakage, demand agile, reliable, and automated machine learning deployment strategies. This paper proposes a comprehensive CI/CD-integrated model specifically designed to streamline the deployment of machine learning models for revenue risk prevention in financial environments. By combining modular pipeline architecture with continuous integration and delivery practices, the framework automates critical stages such as data validation, model training, testing, deployment, and monitoring. Key contributions include a layered design that supports real-time model updates, governance mechanisms ensuring compliance and version control, and integrated feedback loops facilitating continuous improvement. The model addresses operational challenges like pipeline drift, stale models, and manual deployment bottlenecks, enabling faster time-to-deploy and reducing human error. Strategic implications involve enhanced risk mitigation, scalable model governance, and resource optimization aligned with regulatory standards such as SOX and GDPR. The proposed framework bridges the gap between DevOps automation and revenue risk analytics, offering both academic insights and practical pathways for scalable, compliant, and resilient machine learning operations. Future research directions suggest incorporating AutoML for enhanced automation, empirical validation in production settings, and extensions towards federated learning and edge deployments to support distributed financial ecosystems.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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2022-10-30

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Volume 8, Issue 5, September-October-2022

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[1]
Babawale Patrick Okare, Tope David Aduloju, Olanrewaju Oluwaseun Ajayi, Okeoma Onunka, Linda Azah, " A CI/CD-Integrated Model for Machine Learning Deployment in Revenue Risk Prevention" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 5, pp.425-438, September-October-2022.