Module 1: GenAI 101 for Insurance

• LLM fundamentals (transformers, context, hallucinations)
• Insurance-specific use cases:
• Underwriting decision support
• Claims automation and summarization
• Fraud detection insights
• Policy document generation and analysis
• 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:
• Claims summarization
• Underwriting analysis
• 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:
• Requirements → user stories → acceptance criteria
• 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:
• Test case generation
• Edge case detection
• 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:
• Policy documents
• Claims history
• 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:
• Pilot → scale → enterprise rollout
• Organizational readiness and change management
• KPIs:
• Claims processing time reduction
• Loss ratio improvement
• Customer satisfaction (NPS)
• Fraud detection accuracy

Workshop: Create a 90-day AI adoption roadmap for:

• Claims
• Underwriting
• Customer experience

This course is ideal for individuals involved in defining, developing, or managing requirements, including:

• Business Customers or Stakeholders
• Product Managers / Customer Representatives
• Business or Systems Analysts
• Architects and Developers
• QA/Test Engineers
• Project Managers or Team Leaders
• IT Managers/Directors

• Explain how LLMs work and where GenAI adds value across insurance value chains

• Apply AI-assisted workflows in underwriting, claims, policy servicing, and fraud 

detection

• Use prompt engineering and AI tools to accelerate development, testing, and business 

workflows

• Integrate AI into SDLC, DevOps, and enterprise systems

• Implement governance, compliance, and model risk controls

• • Define KPIs and rollout strategies for enterprise AI adoption in insurance