Fill out the form below to Download the Whitepaper
AI adoption in U.S. banking is growing rapidly, but many institutions still struggle to move from pilot programs to fully governed production systems. This whitepaper explores the gap between AI pilots and their operational deployment, highlighting the governance structures necessary to scale AI effectively while meeting regulatory requirements.
Discover What This Whitepaper Covers:
Why AI Pilots Fail to Scale
Learn why even successful AI pilots often fail to move into production, from a lack of governance and institutional readiness to the absence of formal risk management structures.
The Importance of AI Governance
Explore the key governance domains banks must address, including AI strategy, model risk management, data governance, and vendor management, ensuring that AI systems are compliant, auditable, and scalable.
Addressing Compliance Challenges
Understand the regulatory landscape surrounding AI in banking, including the need for transparency, explainability, and fair lending compliance, with specific guidance on ensuring adherence to frameworks such as SR 11-7 and the OCC Bulletin 2011-12.
A Roadmap for Practical AI Governance
Follow a practical, phased approach to establishing an AI governance framework that aligns with regulatory expectations. This roadmap includes inventorying AI models, aligning board strategy, and implementing controls for bias testing and vendor oversight.
Key Takeaways from the Whitepaper
- Clear governance structures for AI in banking
- A roadmap for aligning AI with regulatory requirements
- Best practices for managing AI risks, including model validation and data protection
- Steps for reducing fair lending risks and ensuring compliance with AI-driven credit decisions