Financial regulators are facing growing pressure to modernise their technology infrastructure as rising data volumes, increasingly complex markets and new regulatory demands test traditional approaches to supervision.
Regnology’s analysis, drawing on regulatory dialogues and work with more than 100 supervisory authorities globally, identifies three technologies emerging as particularly important to the future of supervision: cloud-native infrastructure, granular data and AI.
Cloud adoption is becoming a key part of this shift, giving supervisory authorities greater flexibility to scale systems and introduce new capabilities. A 2025 Central Banking survey found that 53% of regulators were already using cloud services, while another 27% planned to adopt them.
The Andorran Financial Authority (AFA) illustrates the potential impact. It replaced legacy on-premises infrastructure with Regnology’s Supervisory Hub (RSH), hosted on Rcloud. The transition was completed within five months, helping modernise its supervisory infrastructure while reducing costs and improving efficiency. Report creation and implementation times were reduced from weeks to days.
At the same time, regulators are moving away from aggregated and template-based reporting towards more granular and standardised data. Consistent definitions and greater detail allow information to be reused across supervisory functions, reducing duplication while giving authorities a more complete view of emerging risks.
Several major regulatory initiatives reflect this direction. The European Central Bank’s Integrated Reporting Framework is designed to harmonise statistical reporting across eurozone banks, while 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. Canada’s Office of the Superintendent of Financial Institutions is also modernising its data collection through its Data Collection Modernization Programme.
The combination of cloud infrastructure and richer datasets is creating another opportunity: AI-enhanced supervision. Regulators are increasingly applying machine learning and AI to analytics, automation, stress testing and risk identification, while retaining human oversight.
The Qatar Financial Centre Regulatory Authority is using AI to identify emerging risk factors, while Peru’s banking and insurance superintendency applies machine learning to stress testing. The Central Bank of Brazil is also using similar technologies to compare bank results and identify potentially underestimated risks.
However, advanced supervisory technology brings its own challenges. AI models depend on reliable, well-structured data, while explainability, governance and auditability remain critical considerations for regulators deploying increasingly sophisticated systems.
Regnology is positioning its Ascend platform around this transition, combining cloud infrastructure, governed intelligence and data management. Its RSH platform is designed to support granular reporting, near-real-time supervision, risk calculations, stress testing and early-warning capabilities.
The wider shift suggests supervisory technology is moving from back-office infrastructure towards a strategic component of financial regulation. As regulators face growing data demands and increasingly complex risks, Regnology’s analysis highlights how the combination of trusted data, scalable cloud infrastructure and AI could become central to effective oversight.
Read the full Regnology analysis here.
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