AI in financial supervision across emerging markets: what's real, what's next
AI is moving into the financial sector across emerging markets and developing economies, but most jurisdictions are still at the early end of adoption. A recent World Bank survey of 27 financial authorities shows a clear trend: expectations are positive, implementation is uneven, and supervision is catching up. The near-term wins are operational, while the bigger lift is governance, data, and skills.
Where financial institutions are using AI today
Among jurisdictions with at least early-stage adoption, three use cases dominate: customer service, fraud detection, and AML/CFT and KYC compliance. These are mature, high-ROI applications that don't require a full rebuild of core systems.
- Customer service chatbots and virtual assistants
- Fraud detection and anomaly flags
- AML/CFT and KYC compliance workflows
In Africa, institutions are more likely to apply AI to credit scoring and underwriting to serve thin-file customers. Compliance pressures and the need to meet requirements more efficiently are accelerating adoption across regions.
How authorities are adopting AI
Authorities are testing AI, but most are not yet using it for core supervisory tasks. A minority are piloting tools for data collection, on/off-site supervision, asset quality reviews, and anomaly detection. More common pilots include fraud-related analytics, complaints analysis, and risk and compliance assessments.
Basic GenAI tools are already widespread for drafting and summarisation. Some authorities are exploring AI agents, internal chat tools, and knowledge management. Formal AI use policies are emerging, though only a small share of African authorities report having one in place. Many are mapping supervisory processes to find quick wins, but risk appetite varies by institution.
What's getting in the way
Data is the main constraint. Sensitive data is often fragmented, hard to access, and subject to privacy, security, and localisation rules. Cloud use raises concerns around vendor dependency, sovereignty, and security models.
Legacy infrastructure makes integration costly and slow. Skills gaps are material-especially in Africa-so authorities are working on workforce readiness and targeted hiring. Vendor concentration risk is real, as most depend on a small group of global providers. Consumer risks and model risks-bias, explainability, accuracy, and accountability-are recognised but not fully addressed given the early stage of adoption.
- Top barriers: data privacy and security, internal skills gaps, model risk and validation challenges, and integration with existing systems
For broader context on supervisory issues and AI, see overviews from the BIS Financial Stability Institute and the Financial Stability Board.
Two principles to anchor supervision
First, AI should support-not replace-supervisory judgement. Human supervisors must keep final authority and be able to explain decisions influenced by model outputs.
Second, institutions must understand their models and be accountable for decisions tied to those outputs. Documentation, validation, testing, and clear lines of responsibility are essential.
A pragmatic action plan for authorities (5 steps)
- Governance: Establish board-level oversight that aligns AI initiatives with supervisory objectives and public trust.
- Infrastructure: Build integrated IT and data foundations that can support AI at scale, including clear approaches to cloud integration.
- Talent: Develop repeatable hiring, upskilling, and retention programs. Pair domain expertise with data science, engineering, and model risk skills inside supervision teams.
- Monitoring and risk assessment: Systematically track AI adoption and risks across institutions, bridge data gaps, and update supervisory methodologies.
- Collaboration: Coordinate domestically and internationally to reduce fragmentation and regulatory arbitrage and to manage cross-border risks.
Implications for financial institutions
Expect supervisors to ask for stronger model governance, better data lineage, and clearer explainability. Be ready to show how AI decisions are monitored, validated, and corrected. Clarify cloud strategies, vendor controls, and incident response for AI-enabled services.
Institutions with credible documentation and human-in-the-loop controls will move faster through approvals and audits. Those that treat AI like any other high-risk model-backed by rigorous testing and accountability-will save time and avoid rework.
What to prioritize in the next 12 months
- For authorities: publish internal AI policies, select two to three supervisory use cases with clear KPIs, and launch pilot-to-production pathways with audit trails.
- For institutions: tighten model lifecycle management, extend compliance controls to AI workflows, and stress-test data and vendor dependencies.
Upskilling: build the bench you'll need
Practical skills matter more than slide decks. Focus on data engineering for supervisory datasets, MLOps for auditable workflows, and model risk management for explainability and bias. If you're mapping training paths by role, explore curated options for finance teams and supervisors.
Helpful starting points: curated AI courses by job and a snapshot of AI tools for finance.
Bottom line
AI adoption in supervision is moving from interest to execution. The quickest progress will come from a tight loop: choose focused use cases, build on solid data and controls, develop the right skills, and hold both institutions and models accountable. That's how authorities and markets get the benefits without inviting new risks.
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