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AI Governance in Regulated Financial Workflows

7 chapters · 35 lessons

1. 1. Mapping AI Decision Points in Financial Workflows

   5 lessons

   Identify where models fit inside payment, lending, compliance, and risk operations without breaking controls.

2. 2. Designing Control Frameworks for AI Systems

   5 lessons

   Build governance structures that define model scope, thresholds, escalation rules, and accountability.

3. 3. Model Risk Management in Financial Services

   5 lessons

   Assess and mitigate risks from data quality, bias, drift, and operational failure.

4. 4. Explainability and Transparency for Stakeholders

   4 lessons

   Communicate model behavior to customers, regulators, auditors, and internal teams.

5. 5. Human-in-the-Loop Workflows and Escalation Design

   5 lessons

   Structure review queues, case routing, and override protocols for high-stakes decisions.

6. 6. AI in Payments, Fraud, and Transaction Monitoring

   5 lessons

   Apply governance principles to real-time payment flows, fraud scoring, and dispute resolution.

7. 7. AI in Lending, Credit, and Underwriting

   6 lessons

   Govern credit models with fairness, explainability, and regulatory compliance in mind.