AI-enhanced oversight: The future of predictive supervision

Around the world, supervisory authorities are moving toward AI-powered oversight that shifts risk management upstream and reduces the burden of manual examination cycles. For banks and other regulated entities, this shift raises expectations for integrated data systems, stronger governance, and operational readiness to engage with regulators in near real-time and strengthen resilience across markets.

The panel convened voices from the U.S., Europe, Brazil, and global SupTech innovation networks, each bringing different perspectives yet aligned in their trajectory—toward AI-enabled oversight that benefits both regulators and financial institutions.

Inside the discussion: Key takeaways

Real-time monitoring replaces backward‑looking examinations.
AI unlocks a step change in supervisory effectiveness: instead of periodic, sample-based reviews, supervisors can run comprehensive checks in real time. This enables banks to engage earlier, reducing escalation and remediation costs.
AI adoption is advancing, but readiness varies widely.
According to the State of SupTech Report 2024, which surveyed 134 authorities across 100 agencies, of those who have adopted AI, 26% are piloting AI tools, and nearly 30% have not yet incorporated AI into their supervisory frameworks. As advanced markets move toward AI-driven supervision, readiness gaps shape how regulators and regulated entities experience supervisory modernization.
Strong data foundations are non‑negotiable.
Across jurisdictions, the main barriers have less to do with model sophistication and more with poor data quality, integration into legacy workflows, and data protection, privacy and security constraints. Supervisors emphasized that without coherent taxonomies, robust governance, and clean input data from regulated firms, AI tools risk becoming fragmented.

Granular datasets enable earlier, more targeted interventions.
The Central Bank of Brazil shares what becomes possible when structured data is available at scale. Processing ~8 billion granular records, its ADAM under provisioned loans and credit anomalies, enabling supervisors to act sooner. Domains such as liquidity, credit risk, and FX exposures now operate weekly to near real‑time alert cycles.
Shared responsibility is key for safe AI deployment in supervision.
Supervisory tools rely on multiple layers of data, models, and providers, making clarity on “who is responsible for what” essential. A shared responsibility model reduces ambiguity when issues arise and builds trust, enabling AI to scale safely in sensitive areas like AML and credit‑risk surveillance.

AI-enhanced oversight: Building the future of predictive supervision

"AI allows supervisors to adopt a real-time posture. Instead of reviewing last year’s reports, you begin your day with alerts generated by pattern recognition—showing which banks are developing issues and enabling early remediation."

Prof. Dr. Joachim Wuermeling, Executive in Residence, ESMT Berlin

"If there’s not clear shared responsibility, everyone points the finger at somebody else when something goes wrong — and we don’t want that with AI. When you articulate who is responsible for what upfront, you get clarity, and with that clarity, these systems can really scale with confidence."

Michael Hsu, Office of the Comptroller of the Currency (OCC)

"We built our approach on structured data collected over many years—from financial institutions, clearing houses, and government sources—which became the foundation for reliable early warning systems."

Marco Verrone, Deputy Head of Strategic Management and Specialised Supervision Department, Central Bank of Brazil

"The hard work of putting data strategies, data governance, digital transformation, and SupTech strategy in place pays off. It both increases the number of deployed applications and lowers the severity of challenges agencies face."

Dr. Simone di Castri, CEO, Digital Transformation Solutions and Cambridge SupTech Lab