Financial regulators are under increasing pressure to modernise their technology infrastructure as data volumes rise, markets become more complex and evolving regulatory demands place greater strain on traditional supervisory approaches.
Regnology’s analysis, informed by regulatory dialogues and engagement with more than 100 supervisory authorities worldwide, identifies three technologies converging to reshape the future of supervision: cloud-native infrastructure, granular data and AI.
Cloud adoption is giving supervisory authorities greater flexibility to scale systems and introduce new capabilities without the constraints of legacy infrastructure. A 2025 Central Banking survey found that 53% of regulators had already adopted cloud services, while a further 27% were planning to do so.
The Andorran Financial Authority (AFA) provides an example of how this transition can affect supervisory operations. By replacing on-premises infrastructure with Regnology’s Supervisory Hub (RSH), hosted on Rcloud, AFA modernised its supervisory infrastructure within five months. The transition reduced costs, improved efficiency and cut report creation and implementation times from weeks to days.
At the same time, regulators are moving away from aggregated, template-based reporting towards more standardised and granular datasets. Consistent definitions and greater levels of detail allow data to be reused across supervisory functions, reducing duplication while giving authorities a more detailed view of emerging risks.
Several major initiatives reflect this shift. The European Central Bank’s Integrated Reporting Framework is designed to harmonise statistical reporting across eurozone banks. The Hong Kong Monetary Authority’s Granular Data Reporting 3.0 and Bank Negara Malaysia’s project STREAM are pursuing a “collect once, use many” approach. In Canada, the Office of the Superintendent of Financial Institutions is modernising data collection through its Data Collection Modernization Programme.
The combination of cloud infrastructure and richer datasets is also creating opportunities for AI-enhanced supervision. Regulators are increasingly using machine learning across analytics, automation, stress testing and risk identification, while retaining human oversight.
Examples include the Qatar Financial Centre Regulatory Authority’s use of AI to flag emerging risk factors, Peru’s banking and insurance superintendency’s application of machine learning within stress testing, and the Central Bank of Brazil’s use of similar tools to compare bank results and identify potentially underestimated risks.
However, these technologies also introduce challenges. AI models depend on the quality of the underlying data, while explainability, governance and auditability remain important considerations for regulators deploying increasingly sophisticated systems.
Regnology is developing its Ascend platform around this convergence, combining cloud infrastructure with governed intelligence and trusted data management. Its RSH platform is designed to support granular reporting, near-real-time supervision, risk calculations, stress testing and early-warning capabilities.
The direction of travel suggests supervisory technology is moving beyond back-office infrastructure towards a more strategic role in financial regulation. As data demands and risk complexity continue to increase, Regnology’s analysis highlights the potential importance of combining trusted data, scalable cloud infrastructure and AI to support more effective supervision.
Read the full Regnology analysis here
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Copyright © 2026 RegTech Analyst
