Case Study: Strengthening AI Governance in a global bank

When a leading global financial institution faced regulatory scrutiny over its fragmented AI and machine learning model management, they recognized the need for a centralized “golden source” to ensure transparency, consistency, and compliance. With an ambitious 20-person internal team already working on the initiative, the institution sought external expertise to accelerate progress and enhance their solution’s architecture. Through Murdio’s Experts for Hire service, seasoned technical specialists integrated seamlessly into the client’s team, providing critical guidance and technical skills to drive the project forward. This case study highlights how Murdio’s collaborative approach supported the institution to rapidly establish a scalable, cloud-native AI Inventory Platform tailored to their unique governance needs.

The challenge

A major global financial institution, operating across multiple regions and subject to stringent regulatory oversight, faced a significant gap in its governance of artificial intelligence models. An internal audit revealed that information about AI models was scattered across multiple, disconnected systems — each with different formats, varying data models, and incomplete or inconsistent documentation. The lack of a centralized “golden source” for AI model metadata presented a substantial operational risk, particularly in the context of growing regulatory scrutiny around AI transparency, usage, and risk management.

The solution

To address these challenges, the institution initiated the development of a centralized AI Inventory Platform — a Collibra-like system tailored specifically for AI/ML models. Recognizing the complexity of the initiative, the client sought additional expertise to bolster their existing 20-person team.

Murdio’s Experts for Hire service provided experienced specialists to integrate seamlessly with the client’s internal team, delivering targeted expertise in architecture, integration, and automation.
Key capabilities delivered together with the client’s team:

• API-first architecture: The Inventory was built with a robust, extensible API that allows other systems to register and retrieve model metadata automatically. Any new model developed elsewhere in the ecosystem is now automatically registered in the inventory.
• Seamless integrations: The team implemented integrations with two pre-existing internal tools that previously stored fragmented model information. These integrations now deliver daily data feeds, enriching the centralized inventory and ensuring data consistency.
• Flexible import mechanism: A custom “import API” was developed, inspired by Collibra’s capabilities. It supports importing model data via CSV files mapped through JSON templates, enabling fast onboarding of legacy data and updates.
• User interface: While the front-end UI, built in Angular, is still in its early stages, it already allows users to browse, view, and manage basic AI model metadata through a user-friendly interface.
• Cloud-native deployment: The entire solution is deployed on Microsoft Azure using Azure Web Apps and Azure Functions for scalable integration processing.

Outcomes

Although still in early adoption, the platform is fully functional and already in use by multiple teams. Through their Experts for Hire service, Murdio has helped the client achieve:

• A more consistent and transparent inventory of AI models, enhancing compliance and reducing regulatory risk.
• Improved lifecycle management of AI/ML models, with a clear foundation for ongoing governance, monitoring, and reporting.
• Faster progress towards project milestones thanks to the expertise and seamless collaboration of Murdio’s specialists.

What’s Next

Future plans include expanding the UI’s capabilities, onboarding more systems, and leveraging the inventory for compliance reporting, monitoring, and governance workflows.