Enabling Efficiency in Regulatory Reporting

The International Finance Corporation (IFC) partnered with the Capital Markets Authority of Kenya (CMA) to pilot the use of generative artificial intelligence to strengthen corporate governance supervision and regulatory reporting in Kenya’s capital markets. Leveraging IFC’s MALENA platform, the collaboration tested how AI could support the review of corporate governance disclosures while maintaining human oversight and regulatory accountability. The case study demonstrates how AI can augment regulatory capacity, improve review consistency, and enable regulators to focus more of their time on analysis, judgment, and engagement with issuers.

The partnership was driven by growing demands on CMA’s supervisory function following the implementation of Kenya’s Code of Corporate Governance Practices for Issuers of Securities to the Public, 2015. The code requires listed companies to conduct annual self-assessments and submit extensive supporting documentation for regulatory review. As disclosure volumes increased and reports became more complex, CMA’s manual review process became resource-intensive and time-consuming, limiting the speed and consistency of oversight. MALENA was adapted to address these challenges by rapidly analyzing unstructured disclosure data, validating issuer responses, and generating traceable, evidence-based assessments to support faster, more consistent supervision.

Key Takeaways

• Faster Reviews: AI-enabled supervision reduced review and response times by approximately three months during beta testing.
• Time Savings: Average review time per issuer decreased from 15 business days to 30 minutes following deployment of the MALENA questionnaire hub, representing a 99.6 percent reduction in review time.
• Improved Quality and Consistency: MALENA improved the consistency and quality of reviews by systematically analyzing lengthy disclosure documents, identifying supporting evidence, and generating detailed assessments and recommendations.
• Stronger Supervisory Engagement: By reducing time spent on document review, the platform enabled CMA staff to focus more on analysis, supervisory judgment, and engagement with issuers.
• Streamlined Processes: The solution eliminated several manual processes, including spreadsheet preparation, document searches, and reliance on external resources for disclosure analysis.
• Benefits for Issuers: Issuers are expected to benefit from faster regulatory feedback, lower reporting costs, and improved transparency and disclosure quality.
• Scalable Model: The initiative provides a replicable model for regulators in emerging markets seeking to modernize supervision through responsible AI deployment.
• Broader Market Impact: The collaboration demonstrates how AI can strengthen regulatory capacity, market transparency, and investor confidence while preserving human judgment and accountability.