Artificial Intelligence in the Zimbabwe Banking Sector: A Systematic Literature Review

Abstract

The study methodology employed a systematic literature review (SLR) based on Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). The guidelines were used to find the current state of Artificial Intelligence (AI) adoption in the Zimbabwe banking sector. The research employed a structured PRISMA review protocol to search journal articles from Google Scholar, Research gate and Semantic Scholar. Data were also searched from regulatory and Institutional publications, industry reports, and grey literature published between 2015 and 2025. A total of 381 articles were identified, 328 articles were excluded and 53 were included. The review identified AI applications, reasons for AI adoption, benefits which come with AI adoption, challenges in integrating AI applications and governance issues in the Zimbabwean banking sector. Results indicate that AI adoption in the Zimbabwe banking sector is in its early adoption phase used mainly in customer service automation, reporting systems, and basic operational efficiency tools, while advanced applications such as predictive compliance modelling and AI driven credit analytics remain limited. Barriers to AI adoption are skills shortages, data governance concerns, integration challenges, and regulatory uncertainty. The study contributes to the literature by consolidating fragmented data from various sources, and it also identifies research gaps and recommends a future research trajectory and policy recommendations for AI adoption in the Zimbabwe banking sector.