AI Responsibility Capstone: Governance Under Pressure - Finance Case Study

Course Type

Executive Leadership | AI Governance Capstone Simulation

Institution

C-Lab Institute

Overview

Artificial Intelligence in financial services does not simply optimise decisions — it influences credit access, risk assessment, fraud detection, and market stability.

This capstone is the culminating stage of the C-Lab Institute AI Responsibility pathway. Participants are placed in a regional financial institution preparing to deploy an AI-powered credit risk and customer profiling model.

The model promises:

• Faster loan approvals
• Improved fraud detection
• Higher profitability

However:

• Early testing suggests possible demographic bias
• Model explainability is limited
• Regulators are increasing scrutiny of algorithmic decision-making
• Consumer advocacy groups are demanding transparency
• Investors are pushing for rapid rollout

The board must determine whether to proceed, delay, restrict, or redesign the deployment.

This is not a data science decision.
It is a governance decision under regulatory and reputational pressure.

Capstone Objective

Participants will act as Chair of the AI Risk & Governance Committee and prepare a formal Board Advisory Memorandum recommending a responsible course of action.

The capstone evaluates the leader’s ability to:

• Identify strategic, regulatory, and consumer risks
• Assess model bias and explainability concerns
• Evaluate compliance exposure under financial regulations
• Design oversight and accountability mechanisms
• Propose monitoring, testing, and audit frameworks
• Make a defensible go / no-go deployment recommendation

What Participants Must Submit

A structured 1,800–2,000 word Executive Governance Brief including:

Risk Identification

Strategic risk
Regulatory compliance exposure
Model bias and fairness risk
Explainability and transparency gaps
Consumer protection implications
Financial stability concerns
Reputational and investor risk

Governance Framework Proposal

AI risk oversight committee structure
Defined accountability across business, risk, and IT
Model validation and independent audit process
Human-in-the-loop review for high-risk decisions
Ongoing monitoring and model drift controls
Incident reporting and regulator notification protocol
Customer disclosure and appeal mechanisms

Deployment Decision

Deploy / Deploy with Safeguards / Delay & Strengthen Controls / Suspend

Conditions required for responsible deployment

12-month governance and compliance roadmap

Reflection

“What does responsible AI leadership require when financial performance and regulatory accountability collide?”

Assessment Criteria

Submissions are evaluated on:

• Depth of Regulatory & Risk Analysis
• Governance Framework Strength
• Balance Between Innovation and Prudence
• Clarity of Strategic Decision
• Ethical and Consumer Protection Awareness

Award

🏅 300 Responsibility Coins
📜 C-Lab Institute AI Responsibility Capstone Certificate