Artificial Intelligence and Financial-Sector Supervision in Africa: Opportunities, Risks and a Framework for Responsible Adoption.
TL;DR
The findings showed that AI could strengthen fraud detection, risk-based supervision, early-warning systems, regulatory-reporting analysis and consumer monitoring, however, poor data quality, algorithmic bias, limited explainability, privacy and cybersecurity risks, skills shortages and vendor dependence constrained responsible adoption.
Abstract
Artificial intelligence (AI) is increasingly used in financial services and supervision, yet financial institutions and FinTech firms are adopting it faster than many African regulators can effectively oversee it. This study examined the applications, opportunities, risks and regulatory conditions associated with AI-enabled financial-sector supervision in Africa. It combined a systematic literature review with comparative regulatory analysis covering publications issued between January 2020 and July 2026. Evidence was retrieved from Scopus, Web of Science, SSRN, the IMF, World Bank, Bank for International Settlements, African Development Bank and relevant African regulatory authorities. Following PRISMA procedures, 565 records were identified, 141 duplicates were removed, and 424 records were screened. After full-text and quality assessments, 56 publications were included in the final thematic synthesis. Regulatory arrangements in Nigeria, Ghana, Kenya and South Africa were compared across AI policy, SupTech initiatives, data protection, cybersecurity, consumer protection, regulatory sandboxes, institutional capacity and accountability. The findings showed that AI could strengthen fraud detection, risk-based supervision, early-warning systems, regulatory-reporting analysis and consumer monitoring. However, poor data quality, algorithmic bias, limited explainability, privacy and cybersecurity risks, skills shortages and vendor dependence constrained responsible adoption. South Africa demonstrated the strongest disclosed financial-sector AI readiness, while Kenya had the clearest national AI strategy. Nigeria and Ghana had important digital-finance and cybersecurity foundations but limited publicly documented AI-enabled supervisory applications. The study developed an African Responsible AI–Financial Supervision Framework comprising six connected pillars and a phased adoption cycle. It recommends proportionate implementation, common model-validation standards, stronger inter-agency cooperation, shared regional infrastructure and independent oversight.