<![CDATA[Artificial intelligence (AI) is increasingly embedded in digital banking, yet evidence concerning its actual adoption, operational value, and governance implications in Nigeria remains fragmented. This study provides a PRISMA 2020-guided systematic literature review to examine the emerging opportunities and associated risks of AI-enabled digital banking in Nigeria. Scopus, Web of Science, IEEE Xplore, and Google Scholar were searched for English-language publications issued between 2018 and 2026, supplemented by selected policy and organisational documents. From 3,583 identified records, 1,258 were screened, 236 full texts were assessed, and 89 records were retained. Evidence was appraised using source-sensitive criteria derived from the Mixed Methods Appraisal Tool, AMSTAR 2, and AACODS, and was synthesised through thematic, temporal, technology-use-case, and adoption-maturity analyses. The evidence indicates that AI adoption in Nigerian banking is accelerating but remains uneven and predominantly function-specific. Fraud detection, cybersecurity, customer-service automation, and predictive analytics show the clearest operational uptake, whereas explainable credit scoring, generative AI, and enterprise-wide integration remain emergent. Benefits relating to efficiency, financial inclusion, personalisation, and risk detection are inseparable from data-protection, bias, cybersecurity, model-risk, skills, and infrastructure constraints. The review contributes a Nigeria-specific socio-technical synthesis that links AI capabilities, institutional readiness, adoption maturity, and regulatory safeguards. Sustainable deployment requires privacy-by-design, model validation, human oversight, interoperable digital infrastructure, and coordinated supervision by banking, data-protection, and technology regulators.]]>
