Abstract
Artificial intelligence has become central to how African FinTech firms detect fraud, assess credit risk, and extend financial services to previously underserved populations. However, its rapid deployment has outpaced the governance structures meant to keep it accountable. This study set out to understand how explainable predictive risk intelligence relates to AI governance effectiveness in African FinTech organisations, and, in turn, how that governance effectiveness shapes the success of AI-enabled digital transformation projects. The inquiry drew on the TechnologyOrganization–Environment framework and Dynamic Capabilities Theory to make sense of how firms convert technological capability and regulatory pressure into workable governance practice. Because the aim was conceptual rather than statistical, the study proceeded through a qualitative systematic literature review, synthesised Seventeen evidence sources published between 2019 and 2025, supplemented by foundational theoretical and methodological works from Scopus, Web of Science, Google Scholar, and relevant regulatory and industry publications. The literature was analysed using Jabareen’s (2009) conceptual framework analysis method. The review’s central finding was that, while explainability is widely treated in the literature as a mechanism that strengthens governance, and governance is in turn widely linked to project success, no source retrieved through the search integrated all three constructs such as; explainability, governance, and project success, within an African institutional setting. Existing African-focused scholarship addresses AI regulation or financial inclusion in isolation, leaving governance frameworks that combine predictive risk intelligence with explainability largely theorised outside the continent. The study concluded that sustainable AI-enabled transformation in African FinTech rests less on predictive sophistication than on the institutional capacity to govern that intelligence transparently. It recommended that African FinTech regulators and industry bodies establish mandatory explainability standards, requiring that predictive risk systems used in credit, fraud, and compliance decisions be auditable and interpretable by both supervisors and the institutions that deploy them.
Keywords: Explainable Artificial Intelligence; Predictive Risk Intelligence; AI Governance; African FinTech; Project Success; Digital Transformation.
Author name(s):
Temitope Oluwafemi Ademola,
Business Innovation, School of Business, Osiri University, Lincoln, Nebraska, USA
Fatai Laide Rufai,
Business Innovation, School of Business, Osiri University, Lincoln, Nebraska, USA
Amaka Patience Eneh,
Policy and strategic studies, African university of science and Technology, Abuja
Emmanuel Badu Boateng,
Human Resource and Organizational Development, Kwame Nkrumah University of Science and Technology, Ghana
Jide Gboyega Okedeji,
Business Innovation, School of Business Osiri University, Lincoln, Nebraska, USA
DOI Link: https://doi.org/10.5281/zenodo.23156857
Copyright © [2026] The Author(s). Published by the Journal of People and Worldviews (JPW). This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited.