A tailored course, built for your situation
The go-to practitioner for AI governance in insurance operations
Become the internal expert your team consults first when AI policy decisions arise
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Who this is for
Mid-career risk-inclined professional at a global insurer, working at the intersection of AI deployment and compliance, with exposure to technical standards through academic or project partnerships.
Who this is not for
Executives seeking board-level talking points, entry-level staff needing AI fundamentals, or practitioners outside financial services.
What you walk away with
- Recognized internally as the first point of contact for AI governance queries
- Cite-ready examples from insurance-specific AI deployments
- Precedent files for approving or flagging AI use cases in underwriting and claims
- Clear escalation paths when models exceed policy thresholds
- Templates for documenting AI risk decisions in audit-friendly formats
The 12 modules (with all 144 chapters)
Module 1. Defining AI governance in insurance contexts
Establish the scope of AI governance specific to insurance operations, including underwriting, pricing, and claims handling. Differentiate between permissible automation and high-risk decisioning.
12 chapters in this module
- What counts as AI in insurance
- Regulatory touchpoints by line of business
- Materiality thresholds for disclosure
- Model validation expectations
- Human-in-the-loop requirements
- Documentation depth by risk tier
- Vendor AI vs proprietary models
- Customer notice standards
- Bias testing in claims algorithms
- Audit trail expectations
- Incident reporting protocols
- Policy exception frameworks
Module 2. Mapping AI use cases to compliance domains
Align specific AI applications with existing compliance frameworks including Fair Credit Reporting, Unfair Discrimination, and Data Privacy.
12 chapters in this module
- FCRA applicability in underwriting
- UDAAP red flags in pricing models
- GLBA data handling rules
- State-specific disclosure mandates
- AI in renewal decisions
- Customer opt-out mechanisms
- Explainability for denied claims
- Record retention timelines
- Third-party model oversight
- Model drift monitoring
- Escalation to compliance teams
- Documentation for regulators
Module 3. Building internal credibility on AI policy
Position yourself as the reliable source for AI governance interpretation through consistent output, precedent-setting responses, and accessible guidance.
12 chapters in this module
- Creating go-to reference materials
- Standardizing response templates
- Internal FAQ development
- Cross-functional alignment meetings
- Version-controlled policy notes
- Routing rules for new use cases
- Pre-approval checklists
- Escalation decision records
- Peer consultation logs
- Feedback loops from auditors
- Updating guidance quarterly
- Measuring team adoption
Module 4. Documenting AI risk decisions
Produce clear, defensible records that justify AI use or restriction in specific operational contexts, tailored for auditors and leadership review.
12 chapters in this module
- Risk-tier classification system
- Approval matrix by model type
- Template for exception requests
- Sign-off workflow design
- Versioning decision memos
- Linking policy to control
- Audit-ready file structure
- Redaction standards
- Retention schedule mapping
- Cross-border data rules
- Model revalidation triggers
- Decommissioning protocols
Module 5. Precedent files for common AI scenarios
Develop reusable decision frameworks for high-frequency AI use cases such as customer communication bots, fraud detection, and automated underwriting.
12 chapters in this module
- Chatbot tone and content limits
- Fraud model sensitivity settings
- Auto-decline threshold validation
- Override logging requirements
- Agent notification rules
- Customer appeal pathways
- Bias testing frequency
- Model confidence thresholds
- Escalation to human reviewer
- Performance benchmarking
- Feedback loop integration
- Regulator-facing summaries
Module 6. Internal consultation workflows
Design efficient processes for handling AI governance queries from product, data science, and operations teams.
12 chapters in this module
- Intake form design
- Triage criteria
- SLA definitions
- Tiered response templates
- Escalation paths
- Urgent use case handling
- Cross-team sync meetings
- Knowledge base integration
- Query volume tracking
- Response time benchmarks
- Feedback collection
- Process refinement
Module 7. Vendor AI oversight
Assess third-party AI tools for compliance readiness and integration risk, with clear evaluation criteria and ongoing monitoring.
12 chapters in this module
- Vendor due diligence checklist
- API data flow mapping
- Explainability commitments
- Model update notifications
- Bias audit rights
- Subprocessor oversight
- Incident response SLAs
- Penetration testing access
- Audit log access
- Contractual safeguards
- Performance degradation alerts
- Exit strategy provisions
Module 8. AI incident response planning
Prepare standardized responses for AI-related incidents including biased outputs, system failures, and customer complaints.
12 chapters in this module
- Incident classification tiers
- Notification timelines
- Internal reporting chain
- Regulator communication protocol
- Customer remediation steps
- Root cause analysis template
- Corrective action tracking
- Model retraining triggers
- Public statement review
- Legal hold procedures
- Lessons learned documentation
- Update to governance policy
Module 9. Audit preparation for AI systems
Assemble comprehensive, organized documentation packages that satisfy internal and external audit requirements for AI deployments.
12 chapters in this module
- Audit package checklist
- Control mapping exercise
- Evidence collection workflow
- Gap identification process
- Remediation tracking
- Cross-functional review
- Version-controlled submissions
- Follow-up response drafting
- Audit findings categorization
- Pre-emptive testing
- Stakeholder briefings
- Post-audit update cycle
Module 10. Training teams on AI policy basics
Equip non-specialists with practical knowledge of AI governance boundaries through targeted, role-specific training materials.
12 chapters in this module
- Role-based training paths
- Use case decision trees
- Red flag identification
- Escalation procedures
- Testing comprehension
- Onboarding integration
- Refresh cadence
- Feedback collection
- Common misconception fixes
- Scenario-based drills
- Manager talking points
- Compliance attestation
Module 11. AI governance in mergers and acquisitions
Evaluate AI systems in acquired entities and integrate them into existing governance frameworks efficiently and consistently.
12 chapters in this module
- Due diligence scope
- Gap assessment framework
- Integration roadmap
- Policy alignment steps
- Control harmonization
- Team consolidation
- Technology rationalization
- Risk tolerance mapping
- Reporting structure design
- Timeline for compliance
- Stakeholder communication
- Post-close audit plan
Module 12. Sustaining governance maturity
Implement feedback systems, performance metrics, and improvement cycles that keep AI governance responsive and credible over time.
12 chapters in this module
- KPI selection
- Quarterly review cadence
- Stakeholder interviews
- Benchmarking against peers
- Regulatory change monitoring
- Policy update process
- Team capability development
- External validation
- Lessons learned integration
- Technology watch process
- Succession planning
- Annual governance report
How this maps to your situation
- Responding to internal AI policy questions
- Approving or flagging new AI use cases
- Preparing for internal or external audits
- Onboarding new team members to governance standards
Before vs. after
Before
AI governance questions are scattered, responses vary, and precedent is hard to find.
After
Team members know exactly where to go, what to expect, and trust your call as definitive.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3 hours per module, designed to be completed at your pace over 6-8 weeks.
How this compares to the alternatives
Unlike generic AI ethics courses, this program focuses on real insurance operations, documented decisions, and internal credibility, not abstract principles.
Frequently asked
How is this different from general AI ethics training?
It’s built for insurance practitioners who need to make binding decisions on AI use in underwriting, pricing, and claims, with templates, precedents, and audit-ready outputs.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant for global teams?
Yes, it includes jurisdiction-specific considerations and cross-border data rules relevant to multinational insurers.
$199 one-time. Approximately 3 hours per module, designed to be completed at your pace over 6-8 weeks..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours