Today’s financial landscape is defined by continuous evolution, driven by technological innovation, shifting economic conditions, and emerging geopolitical factors. Central banks and regulators are embracing a more data-driven and forward-looking approach to supervision, leveraging advanced technology to modernize oversight.
In this final installment of our in-depth survey series with Central Banking, supervisory leaders share their approaches. These exclusive case studies detail how they are designing more adaptive stress tests and leverage data analytics, and SupTech to identify emerging risks, enabling more timely and informed decision‑making.
Exclusive insights from supervisory leaders at:
Central Bank of Brazil
Qatar Financial Centre Regulatory Authority (QFCRA)
National Bank of Georgia
Superintendency of Banking, Insurance and Private Pension Fund Administrators, Peru (SBS, Peru)
Key areas of focus include:
Emerging risk identification: How regulators are applying data analytics and SupTech solutions to identify and mitigate new risks.
Dynamic stress testing: The role of digitization and automation in advancing stress testing capabilities.
Data governance for supervision: Strategies to improve data quality and collection to ensure accuracy and relevance of key supervisory metrics for informed decision making.
For example, a sharp rise in future interest rates or exchange rate volatility, possibly due to global geoeconomic tensions, the BCB's analysis systems already signal which banks are most exposed to this variation, whether due to interest-sensitive positions or concentration in certain markets. Data models and artificial intelligence are also used experimentally to anticipate risk trends.
Aristides Andrade Cavalcante Neto
Central Bank of Brazil
Automated platforms can enable real-time data ingestion from multiple sources, including market data feeds and macroeconomic indicators. This integration allows for more frequent and comprehensive stress testing scenarios that can capture emerging risks more effectively.
Tatia Tsiklauri
National Bank of Georgia
Data accuracy is very important. But continuous improvement and monitoring relevance – through collaboration, talking to others, looking at what others are doing, sharing what we are doing – is just as critical.
Ewald Müller
Qatar Financial Centre Regulatory Authority
At present, the Superintendency closely monitors significant variations in key supervisory indicators, analyses the reasons behind these changes, and determines whether they stem from risk developments or indicate opportunities to improve the calculation and reporting of these indicators.
Superintendency of Banking, Insurance and Private Pension Fund Administrators
Peru
We have identified gaps in relevant processes, such as data governance and related party management and the need to improve integration of risk management practices across all supervised entities. These are not just tech gaps, but process challenges.
Andrei Cardoso Vanderlei
Central Bank of Brazil
Working closely with regulators, we focus on supporting the transition towards more data‑driven and forward‑looking supervision. Regnology Supervisory Hub (RSH) Ascend brings together capabilities such as risk calculation, scenario‑based stress testing and predictive analytics, helping supervisors strengthen the identification of emerging risks and key indicators. With an emphasis on transparency, auditability and human oversight, it supports more efficient processes across the supervisory cycle, from data collection through to validation, enabling more timely and informed decision‑making.
Antoine Bourdais
Suptech Product Director
Regnology
RegTech and SupTech in central banks: 2026 Case Studies
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