Govern and Map a Financial Services AI Use Case
v1.0
A practical guide to defining purpose, roles, boundaries, data, affected people, and regulated decision limits.
This online course helps financial-services teams use generative AI with a defined purpose, bounded data, accountable human review, measurable quality checks, and a recoverable incident path.
Scope includes customer-service draft support, fraud and compliance case triage assistance, document intake, internal knowledge retrieval, quality assurance, and workflow summarization. It excludes autonomous credit, underwriting, pricing, investment, claims, suspicious-activity, eligibility, or personalized financial-advice decisions.
Reference basis: NIST AI Risk Management Framework and NIST AI RMF Generative AI Profile.
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Textbook-section content reused across this course's scheduled class options.
v1.0
A practical guide to defining purpose, roles, boundaries, data, affected people, and regulated decision limits.
v1.0
A practice guide for evaluation, fairness checks, provenance, monitoring, incident response, and continuous improvement.
v1.0
Define accountable ownership, permitted use, prohibited decisions, and change boundaries for financial-services AI.
v1.0
Map AI risks and design representative tests for quality, privacy, security, fairness, and explainability.
v1.0
Create monitoring signals, incident response paths, rollback plans, and evidence for responsible AI operations.
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Live online practice governing, testing, and managing a bounded financial-services AI use case.
Define accountable ownership, permitted use, prohibited decisions, and change boundaries for financial-services AI.
Map AI risks and design representative tests for quality, privacy, security, fairness, and explainability.
Create monitoring signals, incident response paths, rollback plans, and evidence for responsible AI operations.
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