Course Format and Delivery

Delivery Method: LiveOnline
Schedule: 3 sessions of 4.5 hour
Cost: $1,350 USD

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

This immersive bootcamp equips insurance professionals (IT, underwriting, claims, actuarial, operations, and digital teams) with the knowledge and guardrails to safely and effectively leverage Generative AI in a highly regulated environment.

Participants will learn how to apply GenAI across underwriting, claims processing, fraud detection, customer experience, and regulatory reporting, while adhering to data privacy, model risk management, and compliance standards (NAIC, SOC2, GDPR, HIPAA where applicable)

In this course you will learn

• 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

Topics Covered

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