This course is suitable for:

• Underwriting, claims, product development, and operations staff
• Data analysts, actuarial teams, digital/innovation functions
• Compliance, internal audit, legal, and risk management officers
• Insurance leaders overseeing the adoption of AI tools or automation

By the end of this course, participants will be able to:

• Describe key ethical principles relevant to AI use in insurance, including fairness, transparency, accountability, human oversight, and data protection.
• Identify common ethical and operational risks arising from AI-enabled underwriting, pricing, claims automation, fraud detection, and customer interactions.
• Interpret MAS' expectations on responsible AI, particularly the FEAT principles, Veritas methodologies, and relevant aspects of PDPA and model governance.
• Evaluate an AI-related insurance scenario to spot ethical issues or governance gaps and propose practical mitigation measures.
• Apply best-practice governance concepts to ensure responsible, safe, and explainable AI use within insurance organisations.

Topic 1: Introduction to AI in the Insurance Value Chain

• Key applications and benefits of AI across underwriting, claims, distribution, fraud detection, and customer servicing
• Ethical tensions and real-world industry incidents (e.g., Lemonade's biased AI, Optum's claims algorithm controversy)
• Setting the stage: why ethics and governance matter now

Topic 2: Core Ethical Principles for AI in Insurance

• Fairness, bias, and non-discrimination: How AI can perpetuate or amplify unfair outcomes
• Transparency and explainability: Can you explain the decision to a customer or regulator?
• Accountability and human-in-the-loop controls: Who is responsible when AI errs?
• Data ethics and responsible use: Consent, purpose limitation, and data minimisation

Topic 3: Singapore Regulatory Landscape

• MAS FEAT Principles: Fairness, Ethics, Accountability, Transparency
• Veritas Initiative: Industry collaboration on responsible AI adoption
• PDPA and data governance considerations: Obligations for insurers handling personal data
• Model governance expectations: MAS guidelines on model risk management for insurers

Topic 4: Governance Practices for Responsible AI

• Essential components of an AI governance framework: Policy, process, people, technology
• Roles, responsibilities, and oversight: RACI model, AI ethics committees, three lines of defence
• Testing, validation, monitoring, and documentation: Lifecycle governance from development to deployment
• Practical controls insurers can implement: Checklists, red flags, escalation triggers

Topic 5: Scenario Analysis – Ethical Decision-Making in Practice (40 min)

• Participants work through an insurance-specific scenario (e.g., AI flags a claim as suspicious based on non-obvious data patterns, or AI adjusts premium using proxy variables)
• Identify: Risks, ethical considerations, regulatory issues
• Propose: Mitigations and governance measures

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Participant Deliverable: 1-Page Takeaway Guide

Participants receive a reference card including:

• FEAT Principles Quick Reference
• 5 Red Flags for AI Outputs
• 3 Questions Before Approving AI Decisions
• Escalation Triggers Checklist
• Key Resources

Anfernee Tan is a pioneer in solopreneurship strategy and a leading AI educator who has trained over 30,000 entrepreneurs globally in using AI for business. Creator of the Solopreneur Success OS, Anfernee simplifies the use of AI for business owners, showing them how to apply prompt engineering for content creation, productivity, and marketing.

Known for his clarity and hands-on approach, Anfernee has earned a strong following through his courses, newsletters, and speaking engagements. His sessions demystify AI, equipping professionals with practical tools and frameworks to drive immediate, tangible results

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Full Course Fee: S$327.00 (incl. of 9% GST)