AI Ethics in Practice: Financial Services course is for AI ethics professionals working in financial services. Brings IEEE CertifAIEd practice to credit decisioning, fraud detection, model risk governance, systemic risk, and agentic finance, where regulatory scrutiny, fiduciary duty, and market consequences converge
Financial services is one of the oldest and most consequential operational domains for algorithmic decision-making. AI is embedded across credit underwriting, fraud detection, customer onboarding, market surveillance, anti-money laundering, and increasingly algorithmic and high-frequency trading. The regulatory environment is correspondingly dense. SR 11-7, the Equal Credit Opportunity Act, GDPR Article 22, and the EU AI Act all apply concurrently to different parts of the same system.
This microcredential is built for working AI ethics professionals: model risk managers, AI governance leads, compliance officers, and ethics practitioners advising financial institutions. It does not reteach the IEEE CertifAIEd framework. It shows learners how to apply that framework where individual harm, institutional risk, and market-level instability sit on top of one another.
Generative and agentic AI are treated as intensifications of pre-existing concerns about automation, accountability, and oversight, not as categorically new problems. The final module walks through a worked Ethical Issues Register for a fictional credit AI system and positions IEEE CertifAIEd Product and System Certification as a credible third-party evidence pathway for financial institutions seeking to demonstrate trustworthy AI practice to regulators and stakeholders.
IEEE CertifAIEd AI Ethics Professional certificate holders working in, with, or adjacent to financial services. This includes AI governance leads, model risk managers, risk and compliance professionals, and ethics practitioners advising financial institutions on responsible AI deployment.
• Introduction
• Module 1: The Financial AI Landscape
• Module 2: Fairness and Non-Discrimination in Financial AI
• Module 3: Transparency, Explainability, and Model Risk Governance
• Module 4: Systemic Risk and the Ethics of Scale
• Module 5: Agentic AI and Autonomous Decision-Making in Finance
• Module 6: Governance, Assessment, and the Path to Certification
• Conclusion
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