AI Explainability: Advance Model Risk Management in Banking
Every week, I speak with bankers about fitting AI into their model risk management programs. AI is not new, but it evolves rapidly. Until recently, regulatory guidance remained light. The Office of the Comptroller of the Currency’s 2021 handbook on Model Risk Management stood as the only reference point.
The Treasury Department’s recent AI initiatives mark the most significant shift in financial AI governance since the Federal Reserve’s SR 11–7 guidance in 2011. This push resulted in six new AI-related publications.
The BPI-FSSCC guidance on AI and explainability caught my attention immediately. It delivers a clear roadmap for this new regulatory landscape. The document addresses the Treasury’s December 2024 AI report, which identified six critical risk categories that traditional guidance misses. These include bias, explainability, and third-party risks.
Take the emerging challenge of Agentic AI. This evolution of generative AI operates autonomously to orchestrate entire workflows. This capability did not exist when SR 11–7 was written, yet it actively transforms financial services today. Institutions with robust explainability frameworks are positioned to deploy these tools effectively.
Securing a Competitive Advantage
The Treasury’s regulatory updates and industry best practices offer more than compliance exercises. They present a clear path to competitive advantage. Institutions that embrace an explainability framework will establish strong positions in AI deployment, client confidence, and regulatory relationships.
Moving Beyond Traditional Model Risk
Generative AI introduces “black box” challenges that existing frameworks cannot manage. The BPI-FSSCC document distinguishes clearly between interpretability (technical understanding of model mechanics) and explainability (business-relevant justification of outputs). Traditional MRM guidance misses this concept entirely.
The shift signals a move toward stakeholder-specific communication. SR 11–7 was built on a one-size-fits-all approach to documentation, whereas the current landscape requires tailored explanations for regulators, internal teams, and clients.
Five Pillars for AI Explainability
The BPI-FSSCC outlines a comprehensive approach to explainability through five critical pillars:
- Governance and risk management frameworks that align with the NIST AI RMF.
- Data governance criteria featuring Data Nutrition Labels.
- Prompting guardrails, including structured prompts, automated filters, and fine-tuning criteria for real-time controls.
- Assurance and testing methodologies beyond traditional validation, including edge cases and bias testing.
- Ongoing risk monitoring for non-deterministic systems, including behavioral drift detection and output distribution tracking.
Data Nutrition Labels stand out as a critical shift. They provide food label-style documentation for AI training datasets. This delivers transparency into model inputs and potential biases. Model owners must educate themselves on this practice, as it will significantly support explainability efforts.
Strategic Implementation & Your 90-Day Action Plan
This guidance provides clear action items for financial institutions utilizing or assessing AI operations.
Immediate Actions:
- Conduct an AI inventory using the document’s use case categorization.
- Assess current explainability capabilities against the five-pillar framework.
- Establish AI-specific oversight committees beyond traditional model risk committees.
- Establish a roadmap of near-term, mid-term, and long-term capabilities to scale risk management frameworks as AI maturity increases.
Medium-Term Positioning:
- Implement Data Nutrition Label pilots for high-risk AI applications.
- Develop stakeholder-specific explanation protocols.
- Enhance third-party AI vendor management to address transparency limitations.
- Assess automated tools to provide continuous observability and monitoring for AI solutions.
Using AI Regulation to Drive Strategic Growth
The bottom line: This regulatory evolution represents more than compliance overhead. It forms the foundation for the next generation of financial AI deployment. Explainability serves as a competitive differentiator and a builder of client confidence. Institutions that recognize this opportunity today position themselves as the market leaders of tomorrow.
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Key Takeaways
- AI explainability is becoming central to model risk management as regulatory expectations evolve beyond traditional frameworks.
- The BPI-FSSCC guidance introduces a critical distinction between interpretability and explainability, filling a major gap in legacy MRM approaches.
- Emerging technologies like agentic AI require stronger governance, transparency, and continuous monitoring capabilities.
- The five-pillar framework provides a practical roadmap for implementing scalable and compliant AI explainability practices.
- Institutions that prioritize explainability now can turn regulatory change into a competitive advantage, strengthening trust and accelerating AI adoption.
