A Reference Governance Model for Cloud Adoption in Regulated Pan-African Financial Institutions

Authors

  • Oluwasile Adesanya Author

DOI:

https://doi.org/10.64751/

Abstract

Cloud adoption in regulated banking is governed almost entirely by frameworks authored for North American and European institutions, where the controlling regulators, data-residency assumptions, and audit conventions differ materially from those facing pan-African banks. This paper proposes the Cloud Adoption Reference Governance (CARG) model, a five-layer framework that binds regulator mandate, decision rights, control assurance, platform engineering, and value tracking into a single operating model purpose-built for institutions supervised under emerging-market central-bank regimes. CARG was derived inductively from a multi-year cloud transformation programme at a top-tier pan-African banking group serving more than 25 million customers across ten countries and subsequently generalised into a reusable reference model. We describe the model's layers, its decision-rights structure, a control catalogue mapped to Central Bank of Nigeria (CBN) supervisory expectations, an exception-governance mechanism, a benefits-tracking scheme, and a phased implementation roadmap. Using a combination of programme outcome data and illustrative crossfunctional analysis, we show that governed adoption under CARG was associated with a 40% aggregate reduction in operational process bottlenecks across high-volume banking functions, while keeping every onboarded workload inside the institution's audit perimeter. The contribution is a transferable governance reference that other regulated emerging-market institutions can adapt rather than reconstruct from first principles, accompanied by appendices providing a worked control-catalogue extract, a governance-forum charter, a KPI catalogue, an adoption-maturity rubric, and a glossary.

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Published

16-06-26

How to Cite

Oluwasile Adesanya. (2026). A Reference Governance Model for Cloud Adoption in Regulated Pan-African Financial Institutions. American Journal of AI Cyber Computing Management, 3(2), 65-84. https://doi.org/10.64751/