As the financial system is changing rapidly, supervisory authorities require “more reach, speed, and precision.” The adoption of Artificial Intelligence (AI) has become a necessity to ensure supervisory processes “remain in equilibrium with the new financial system dynamic” and to secure financial stability.
In this second of our three-part in-depth survey series conducted by Central Banking, supervisory leaders from across the globe reveal how they are already using or planning to use AI to enable a proactive approach, where the technology flags anomalies for human experts to investigate—significantly streamlining regulatory compliance monitoring and enhancing risk assessment capabilities.
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:
Improved efficiency: Leveraging integrated data management and AI/ML to enhance financial supervisory operations.
Enhanced risk management: Strengthening risk assessment and streamlining regulatory compliance monitoring with new technologies.
Current AI adoption: Showcasing real-world examples of how AI is applied in supervisory practices today.
Future AI roadmap: Highlighting supervisors’ planned initiatives and strategic priorities for AI adoption.
The greatest added value of AI and integrated data management lies in strengthening the preventive and corrective capacity of the Superintendency — enabling earlier risk detection, more timely responses to deviations and better informed decision-making.
Superintendency of Banking, Insurance and Private Pension Fund Administrators
Peru
Integration and AI allow the BCB to refine risk assessment dynamically. Risk models can be directly fed by integrated data and frequently recalibrated with ML, incorporating new data as it arrives. This means that the risk view is always updated and based on the largest possible set of evidence.
Aristides Andrade Cavalcante Neto
Central Bank of Brazil
We have several AI enhancements planned. We are developing more sophisticated anomaly detection models to identify unusual patterns in financial institution data that might indicate compliance issues or emerging risks.
Tatia Tsiklauri
National Bank of Georgia
One thing we just started experimenting with is a tool where we can ask supervisors to provide a narrative description of risk at firms. They can do that completely and freely in words. Then we use LLMs to extract risk factors and definitions from it.
Perttu Korhonen
Qatar Financial Centre Regulatory Authority
As AI becomes increasingly integrated into financial system processes – such as credit rating assessments – supervised institutions will be required to clearly articulate how specific outcomes were reached. This demand for transparency and accountability in AI-driven decisions underscores the importance of explainability in regulatory oversight.
Andrei Cardoso Vanderlei
Central Bank of Brazil
Regulators and central banks are moving beyond chatbots and search toward governed, agent-enabled intelligence that supports every stage of the supervisory lifecycle. Our Regnology Supervisory Hub (RSH) Ascend uses a new AI-enabled orchestration layer within the Ascend platform to advance our vision for Straight-Through-Reporting, always with human-in-the-loop oversight and full auditability.
For supervisors, this means RSH Ascend's governed workflows can now enable true 'straight-through supervision'—streamlining processes, from data collection and validation to surfacing real-time risk signals while ensuring every action remains transparent, explainable and under expert control.
Antoine Bourdais
SupTech Product Director
Regnology
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