Why it matters
AI is outpacing traditional risk frameworks. Banks must now pivot from static oversight to dynamic AI governance to meet E-23 standards. This shift mirrors the broader rise of agentic AI across global enterprise sectors.
The Big Picture
The Toronto summit gathered 100+ leaders to audit the global audit of financial stability.
- Regulatory Pressure. Navigating OSFI E-23 and complex vendor model validation.
- AI Defense. Strengthening oversight and navigating AI-driven risks across the three lines of defense.
Key Takeaways
1. Generative AI Validation Traditional frameworks fail LLMs. The 2026 focus is on fairness and adversarial testing to mitigate algorithmic bias.
2. Shifting Definitions Model inventories are expanding to include AI/ML and vendor tools, requiring deeper documentation and clearer ownership.
3. Audit Ready Innovation Accelerating AI in AML must not break the control environment. Oversight must remain as fast as the algorithms.
The Bottom Line
The era of experimental AI in banking is over. In 2026, rigorous oversight is the only path to scalable innovation.
