Building a Financial Business Case for AI Governance and Runtime Controls in a Bank

Business case structure

The case is built from four components. Each one is developed in the steps that follow.

Why conventional ROI models fit poorly

AI governance and runtime controls do not generate revenue directly. Their value comes from reducing exposure and from enabling AI use cases to be approved and deployed. For that reason, the case should be presented as risk reduction and deployment enablement, not as conventional revenue ROI.

Establish the cost baseline

Start with what the bank spends today. The baseline should span technology, compliance and operations, and cover the following cost areas:

• Technology
• Compliance labor
• Operations
• Model-risk effort
• Audit effort

Quantify avoided-loss and compliance exposure

Cover operational, compliance and security exposure, and state the assumptions behind each scenario. Express every scenario as frequency and severity, with an explicit estimate of how much each control changes them.

Link governance to deployment velocity

Measure the link between governance and AI deployment throughput. The two measures to track are time-to-approval and throughput for AI use cases.

Choose metrics and present the case

Select a small set of KPIs. Each should have a baseline, an owner and a review cadence. When presenting to the investment committee, frame the case as risk reduction and deployment enablement.

Mapping runtime controls to financial line items

The final task is to relate each runtime control to the line items in your business case. Three controls to map are:

• Runtime policy enforcement
• Agent permissions
• Audit logging

For each control, identify the cost and risk drivers it relates to, using the cost baseline, loss scenarios and deployment measures from the steps above.