Case Study: AI Governance & Model Risk Management Framework

Banking

AI Governance & Model Risk Management Framework

The Challenge: A Model Risk Blind Spot in a Regulated Environment

A US regional bank with $85B in assets had steadily expanded its use of machine learning over five years, deploying models across credit underwriting, fraud detection, customer attrition, and interest rate risk. By the time an OCC examination flagged significant gaps, over 60 ML models were in production — yet fewer than a quarter had formal validation documentation, monitoring thresholds, or defined fallback procedures. The bank's existing model risk management policy, designed for traditional statistical models, did not address the complexity of modern ML systems: distributional drift, feature importance shifts, or protected-class disparate impact. Regulators cited SR 11-7 non-compliance and demanded a remediation plan within 90 days. Internally, there was no centralized model inventory, no standard development lifecycle, and model ownership was scattered across five separate teams with no common governance structure.

Our Solution: Enterprise AI Governance Framework

Lydatum designed and implemented a comprehensive AI governance and model risk management framework over a six-month engagement, structured around four pillars:

• Model Inventory and Risk Classification: We built a centralized model registry using MLflow, cataloguing all 60+ production models with standardized metadata — model purpose, development methodology, data inputs, business owner, risk tier, and last validation date. Models were classified into three risk tiers (high, medium, low) based on regulatory impact, decision reversibility, and population affected, which determined validation depth and monitoring frequency.
• Validation and Development Lifecycle: We defined a standardized model development lifecycle (MDLC) with mandatory gates at each stage — data validation, model selection, backtesting, independent validation, deployment review, and ongoing monitoring. For high-risk models, we implemented SHAP-based explainability reports and champion-challenger testing frameworks to ensure robust performance comparison.
• Bias Monitoring and Responsible AI Controls: Using Evidently AI, we deployed automated fairness monitoring for all consumer-facing credit models, tracking disparate impact ratios across protected classes with alerting thresholds aligned to CFPB guidance. Model performance dashboards provided real-time visibility into drift, accuracy degradation, and population stability.
• Governance Operating Model: We established a Model Risk Committee with defined membership, escalation protocols, and quarterly review cadence. A 150-person training program covering model risk fundamentals, documentation standards, and the new MDLC was delivered across model development and business teams.

The Impact: Full Compliance and a Culture of Responsible AI

The bank achieved full SR 11-7 compliance within six months and received a positive assessment from regulators at the follow-up examination. Beyond compliance, the governance framework became a foundation for accelerating model deployment — with clearer standards, teams moved faster with greater confidence.

Model deployment cycle time dropped by 30% as teams leveraged standardized templates and pre-approved tooling. The centralized model registry became the single source of truth for model inventory, enabling the Chief Risk Officer to report on the full model portfolio to the board for the first time. The bank subsequently engaged Lydatum to extend the framework to cover generative AI systems as their LLM-based applications reached production.

Services Delivered: AI Governance & Ethics, AI Maturity Assessment, ML Model Development, MLOps & Reliability

Technologies Used: MLflow, Evidently AI, SHAP, Python, Grafana, Confluence (governance documentation)