AI Governance for Banks: Building Frameworks That Satisfy Regulators and Enable Innovation

Banks have been using machine learning models for years — credit scoring, fraud detection, anti-money laundering. But generative AI changed the conversation. Regulators who were comfortable with traditional ML models are not comfortable with large language models that cannot explain their decisions.

AI governance is the bridge between innovation and compliance. Without it, banks either ban AI (losing competitive advantage) or deploy AI uncontrolled (risking regulatory action). With it, banks can deploy AI in production while satisfying regulators that the models are fair, explainable, and auditable.

Who Is This Guide For?

This guide is for CTOs, model risk officers, and compliance leads at banks building AI governance frameworks. If you need to deploy AI models in production while satisfying regulatory requirements, this is for you.

By the End of This, You’ll Know…

• Why AI governance is a regulatory requirement, not a nice-to-have
• How to build model risk management frameworks that satisfy OCC and Fed requirements
• The explainability techniques that make AI decisions auditable
• How to balance innovation with regulatory compliance

Why AI Governance Matters

Regulators have made clear that AI governance is mandatory:

• OCC guidance: Banks must have model risk management frameworks for all models, including ML and AI
• SR 11-7: Federal Reserve guidance on model risk management that applies to AI models
• EU AI Act: Upcoming regulation that classifies AI systems by risk level and imposes requirements
• Explainability: Regulators require explanation of AI decisions that affect customers

An AI model that cannot explain its decisions is a model that cannot be audited. An unauditable model is a model that cannot be deployed in a regulated environment.

Model Risk Management

Model Inventory

Maintain a complete inventory of all AI models:

• Model name and version
• Business purpose
• Data sources
• Risk tier (high, medium, low)
• Owner and approver
• Last validation date

Model Validation

Every model must be validated before deployment:

• Performance testing: Does the model meet accuracy requirements?
• Fairness testing: Does the model discriminate against protected classes?
• Stability testing: Does the model perform consistently across different data segments?
• Explainability testing: Can the model explain its decisions?

Model Monitoring

Monitor models in production for drift and degradation:

• Performance monitoring: Track accuracy, precision, and recall over time
• Data monitoring: Track input data distributions for drift
• Fairness monitoring: Track fairness metrics across protected classes
• Explainability monitoring: Track explanation quality over time

Explainability

Why Explainability Matters

Regulators require explainability for three reasons:

1. Customer protection: Customers have the right to know why they were denied credit or flagged for fraud
2. Fairness: Explainability enables detection of discriminatory decisions
3. Audit: Auditors need to understand model decisions to verify compliance

Explainability Techniques

• SHAP (SHapley Additive exPlanations): Explains individual predictions by attributing them to input features
• LIME (Local Interpretable Model-agnostic Explanations): Explains individual predictions by approximating the model locally
• Feature importance: Shows which features contribute most to model decisions
• Decision rules: Converts model decisions into human-readable rules

Regulatory Requirements

OCC Requirements

The Office of the Comptroller of the Currency requires:

• Model inventory: Complete inventory of all models with risk tiering
• Independent validation: Models validated by parties independent from model development
• Ongoing monitoring: Continuous monitoring for model performance and drift
• Documentation: Comprehensive documentation of model design, development, and validation

SR 11-7 Requirements

The Federal Reserve’s SR 11-7 guidance requires:

• Model risk management: Framework for identifying, measuring, and managing model risk
• Governance: Board and senior management oversight of model risk
• Development and implementation: Sound development practices including data quality and model design
• Ongoing monitoring: Continuous monitoring and periodic revalidation

What You Can Actually Use Today

• SHAP: Open-source explainability library
• LIME: Open-source explainability library
• MLflow: Machine learning lifecycle management
• Weights & Biases: Experiment tracking and model monitoring

FAQ

How do we explain LLM decisions?

LLMs are harder to explain than traditional ML models. Use retrieval-augmented generation (RAG) to ground LLM responses in specific documents. Log which documents were retrieved and used for each response.

What is the penalty for non-compliance?

Regulators can issue consent orders, impose fines, or restrict AI usage. The OCC has issued consent orders for model risk management failures with fines of $10-50 million.

How often must models be revalidated?

High-risk models: annually. Medium-risk models: every 2 years. Low-risk models: every 3 years. More frequent revalidation is required if model performance degrades.

We help banks build AI governance frameworks that satisfy regulatory requirements while enabling innovation. If you need to implement model risk management, get in touch.