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Article
AI Approval Crisis Exposes Hidden Failures in Bank Governance
As banks accelerate AI adoption, model risk teams are struggling to keep pace with approval demands. Michael Talbert argues the real issue is not speed but flawed governance design. Structural failures, misclassification, and late involvement are creating bottlenecks, forcing institutions to rethink how AI is governed, particularly in high-risk regulatory functions.
Mar 25, 2026
Michael Talbert, Head of Professional Services, Behavox
Tags:
AI and Technology (including Fintech)
The views and opinions expressed in this content are those of the thought leader as an individual and are not attributed to CeFPro or any other organization
- AI approval pressure
rising as deployment accelerates and scrutiny increases
- Uniform governance
frameworks creating bottlenecks across use cases
- Leading firms
classify AI by approval burden before validation begins
- Structural failures
driving most approval breakdowns not model quality
- Late MRM involvement
and poor documentation key issues
- Misclassification
exposing gaps between perceived and actual risk
- Regulated functions
require higher standards of governance
- Agentic AI
introducing new validation and accountability challenges
- Early governance
involvement critical to approval success
- Model risk shifting
from gatekeeper to governance partner
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