AI Model Risk Governance

A large bank used 500+ AI/ML models across credit risk, fraud, marketing, and operations, but lacked a centralized governance framework to manage model risk. Model inventories were maintained in spreadsheets, validation was inconsistent, and regulatory scrutiny of AI model risk was intensifying. The bank partnered with Vitosha to build an enterprise model risk governance platform on Azure ML and Purview, establishing a centralized model inventory, automated validation workflows, and comprehensive risk reporting.

The bank’s 500+ AI/ML models were managed across business units with no centralized inventory, and 30% of models were undocumented. Model validation was inconsistent — critical models received thorough validation, while many moderate-risk models were never independently reviewed. Regulatory guidance (SR 11-7, ECB TRIM, and emerging AI regulations) required comprehensive model risk management including inventory, validation, ongoing monitoring, and reporting. Model documentation was scattered across shared drives and SharePoint sites. The bank had received regulatory feedback on model risk management gaps in two prior examinations and needed to demonstrate a robust, enterprise-wide governance framework.

Phase 1: Model Inventory and Classification — Built a centralized model inventory in Dataverse, cataloging all 500+ models with metadata including purpose, algorithm type, input/output data, risk tier (low, moderate, high, critical), business unit, and regulatory exposure, with automated discovery from Azure ML workspaces and Python model registries. Phase 2: Validation Workflow Automation — Implemented automated validation workflows in Power Automate and D365 Customer Service, routing models through staged validation (conceptual soundness, outcomes analysis, ongoing monitoring) based on risk tier, with automated evidence collection, findings tracking, and remediation workflows. Phase 3: Governance Reporting — Developed Power BI dashboards for the Model Risk Committee and regulators, providing real-time visibility into model inventory completeness, validation status, findings and remediation, risk tier distribution, and key risk indicators, with drill-through to individual model detail and full audit trail.

100% of models inventoried and classified by risk tier, with complete documentation captured in the central system. Model validation cycle time reduced by 50% through automated workflows and standardized evidence collection. Model risk coverage reached 90%, with all high and critical models validated within regulatory timelines. The bank passed its next regulatory examination with no model risk management findings, compared to two findings in the prior exam. The platform supported onboarding of new models at a rate of 10–15 per quarter with consistent governance from day one, and automated ongoing monitoring flagged performance degradation for 8% of models in the first year, enabling proactive remediation.

Enterprise AI model risk governance transformed the bank’s ability to manage model risk at scale. A centralized inventory, automated validation, and comprehensive reporting addressed regulatory expectations while improving the efficiency and consistency of model risk management across the organization.