Module 1: GenAI 101 for Insurance
• LLM fundamentals (transformers, context, hallucinations)
• Insurance-specific use cases:
o Underwriting decision support
o Claims automation and summarization
o Fraud detection insights
o Policy document generation and analysis
o Customer service automation (chatbots, call summaries)
• Risks: bias in underwriting, regulatory violations, data privacy
Hands-on Lab: Identify 5 high-value use cases (e.g., claims triage, underwriting risk scoring);
classify by risk, compliance impact, and ROI
Module 2: Governance, Compliance & Model Risk
• Regulatory landscape (NAIC guidelines, data privacy laws, internal compliance)
• Model risk management (MRM) and explainability
• AI ethics: fairness, bias detection, auditability
Workshop: Create an Insurance AI Governance Framework including:
• Acceptable use policies, Model validation checkpoints, Audit trails and explainability
requirements
Module 3: Prompt Engineering for Insurance Workflows
• Prompt design for business scenarios:
o Claims summarization
o Underwriting analysis
o Policy interpretation
• Human-in-the-loop validation patterns
Lab: Use prompts to:
• Summarize claims documents
• Generate underwriting insights
• Draft policy explanations for customers
Module 4: AI for Engineering & Product Development
• AI-assisted development (Copilot, code generation)
• Accelerating API development for insurance platforms
• Documentation automation for compliance
Lab: Generate:
• Claims processing API
• Test cases and documentation using AI tools
Module 5: AI in SDLC & DevOps
• AI integration across:
o Requirements → user stories → acceptance criteria
o Code → testing → deployment
• Traceability and auditability in regulated environments
Lab: Use AI to:
• Convert business requirements into user stories and test cases
• Generate code and track outputs for compliance
Module 6: Testing, QA & Validation
• AI-assisted testing:
o Test case generation
o Edge case detection
o Regression automation
• Validation requirements for insurance systems
Lab: Generate and execute:
• Test scenarios for claims workflows
• Validate underwriting rules
Module 7: DevOps, Observability & Risk Monitoring
• AI-enhanced monitoring and anomaly detection
• Detecting fraud patterns and system anomalies
• AI-assisted incident management
Lab: Simulate:
• Fraud detection scenario
• AI-driven anomaly analysis
Module 8: Data, RAG & Intelligent Insurance Systems
• Retrieval-Augmented Generation (RAG) for:
o Policy documents
o Claims history
o Regulatory guidelines
• Secure data access and governance
Lab: Build a RAG-based assistant for:
• Claims adjusters
• Underwriters
Module 9: Adoption Strategy, Metrics & Scaling
• AI adoption roadmap:
o Pilot → scale → enterprise rollout
• Organizational readiness and change management
• KPIs:
o Claims processing time reduction
o Loss ratio improvement
o Customer satisfaction (NPS)
o Fraud detection accuracy
Workshop: Create a 90-day AI adoption roadmap for:
• Claims
• Underwriting
• Customer experience