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CMP1760 Mastering ISO 42001 for Insurance Risk and Compliance Practitioners

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Insurance Risk and Compliance Practitioners

A complete implementation guide tailored to practitioners in regulated insurance environments

$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.
Avoid scrambling when AI governance requests land with tight deadlines

Who this is for

Insurance compliance practitioners at global IT services firms handling regulated AI deployments

Who this is not for

C-suite executives looking for high-level AI strategy overviews

What you walk away with

  • Own the end-to-end AI governance documentation process aligned with ISO 42001
  • Produce regulator-ready artefacts on demand without escalation delays
  • Gain documented ownership of AI assurance workflows that survive leadership changes
  • Structure internal AI control reviews that pass QA on first submission
  • Become the default recipient for peer escalations on AI compliance gaps

The 12 modules (with all 144 chapters)

Module 1. Why ISO 42001 Now Applies to Insurance AI Deployments
Understand how ISO 42001 closes gaps in existing frameworks for AI used in underwriting, claims automation, and risk scoring.
12 chapters in this module
  1. Mapping insurance-specific AI use cases to ISO 42001 clauses
  2. How regulators are citing ISO 42001 in recent exam findings
  3. Differentiating ISO 42001 from NIST AI RMF in practice
  4. When to apply ISO 42001 over internal model governance policies
  5. Integration points with existing SOX and DORA compliance cycles
  6. Case: How a European insurer passed audit with ISO 42001 mapping
  7. Common misconceptions about scope in insurance settings
  8. Aligning with global privacy frameworks under Article 25 GDPR
  9. How CGI teams are documenting AI conformity claims
  10. Tools to track evolving AI lifecycle stages under the standard
  11. Building evidence trails for third-party AI model providers
  12. Documenting human oversight mechanisms in claims processing
Module 2. Structuring the AI Governance Statement of Applicability
Build a defensible SoA that reflects actual insurance operations, not just checkbox compliance.
12 chapters in this module
  1. Starting with business context, not control lists
  2. Prioritizing clauses based on underwriting AI exposure
  3. Documenting rationale for exclusion in risk-based terms
  4. Linking SoA decisions to existing enterprise risk registers
  5. How to justify partial implementation of Clause 8.3
  6. Including third-party claims platforms in scope decisions
  7. Version control for SoA updates during AI model retraining
  8. Using SoA responses to guide internal audit sampling
  9. Aligning SoA language with internal audit terminology
  10. Avoiding overreach that invites deeper regulator scrutiny
  11. Common pitfalls in documenting AI model monitoring
  12. Working examples from life insurance AI deployments
Module 3. AI Risk Assessments That Hold Up Under Review
Conduct assessments that anticipate both internal QA and external examiner challenges.
12 chapters in this module
  1. Defining AI asset inventory for actuarial and underwriting models
  2. Setting assessment thresholds based on policy volume risk
  3. Incorporating bias testing into standard risk scoring workflows
  4. Documenting fairness metrics for claims adjudication models
  5. Mapping model drift to regulatory reporting triggers
  6. Integrating with existing model risk management frameworks
  7. Scoping third-party AI tools used in broker submissions
  8. Using ISO 42001 Annex B to structure threat modeling
  9. Common gaps in documenting explainability requirements
  10. Linking risk treatment decisions to business impact levels
  11. How to document residual risk acceptance by senior actuary
  12. Case example from a multi-jurisdictional claims AI rollout
Module 4. Building AI Documentation That Survives Team Changes
Create artefacts that remain useful even when personnel shift , a critical need in project-based consulting.
12 chapters in this module
  1. Template design for consistent AI register updates
  2. Standardizing naming conventions across insurance domains
  3. Version control strategies for AI model documentation
  4. Linking artefacts to change management tickets in ServiceNow
  5. Capturing tribal knowledge before consultant rotation
  6. Documenting data lineage for AI training sets in claims
  7. Using metadata tags to support audit sampling
  8. Creating index pages for AI governance artefact libraries
  9. Structuring handover packs for incoming team members
  10. Embedding ISO 42001 requirements into onboarding
  11. Automating reminders for documentation refresh cycles
  12. Lessons from CGI projects where docs survived leadership changes
Module 5. Internal AI Audit Responses That Pass First Time
Anticipate internal audit questions and deliver complete, concise responses.
12 chapters in this module
  1. Predicting common auditor queries on AI model validation
  2. Organizing evidence by clause to reduce QA cycles
  3. Documenting AI training data provenance for auditors
  4. Creating clear flowcharts for automated claims decisions
  5. Handling auditor requests for model performance data
  6. Preparing for audits during model retraining cycles
  7. Using templates to reduce response turnaround time
  8. Aligning with internal audit's control evaluation criteria
  9. Avoiding over-documentation that invites more scrutiny
  10. How to respond when evidence is not yet available
  11. Case: One team reduced audit follow-ups by 70%
  12. Post-audit review process to improve next cycle
Module 6. Managing Escalations from Peer Teams on AI Gaps
Become the go-to resolver for cross-functional AI compliance issues without becoming a bottleneck.
12 chapters in this module
  1. Setting up triage process for peer AI governance requests
  2. Creating reusable answers for common framework questions
  3. When to escalate vs. resolve locally
  4. Documenting escalation paths for urgent regulator cases
  5. Managing pushback from development teams on controls
  6. Using ISO 42001 to depersonalize compliance debates
  7. Building credibility through timely, sourced responses
  8. Handling requests from non-technical stakeholders
  9. Templates for responding to legal team inquiries
  10. Integrating with existing CGI incident response workflows
  11. Balancing speed and compliance in patch deployments
  12. Case: Resolving conflicting interpretations across regions
Module 7. Vendor AI Reviews That Don't Stall
Drive accountability through third-party providers while maintaining delivery timelines.
12 chapters in this module
  1. Structuring AI-specific questions in vendor assessments
  2. Evaluating third-party model cards for completeness
  3. Assessing vendor claims about bias testing rigor
  4. Reviewing documentation for AI retraining pipelines
  5. Setting expectations for access to model performance data
  6. Using ISO 42001 as a benchmark for contract clauses
  7. Managing vendor responses within tight RFP cycles
  8. Documenting risk acceptance for critical vendor AI
  9. Coordinating with procurement on compliance terms
  10. Handling lack of vendor cooperation on audit access
  11. Case: Aligning two vendors on common AI logging standards
  12. Creating vendor scorecards based on ISO 42001 adherence
Module 8. AI Incident Response Under ISO 42001
Prepare for AI system failures with defined reporting and remediation workflows.
12 chapters in this module
  1. Defining what constitutes an AI incident in insurance
  2. Setting thresholds for reporting model performance drift
  3. Documenting root cause analysis for explainability failures
  4. Integrating with CGI’s existing incident management system
  5. Notifying regulators under AI-specific timelines
  6. Handling customer complaints about AI-driven denials
  7. Preserving evidence during AI incident investigations
  8. Conducting post-mortems with model development teams
  9. Updating risk assessments after incident resolution
  10. Training frontline staff to identify AI-related issues
  11. Case: Rapid response to claims processing bias alert
  12. Preventing recurrence through control updates
Module 9. AI Assurance Work That Earns Repeat Requests
Deliver work that builds trust and generates follow-on demand.
12 chapters in this module
  1. Designing deliverables for reuse across engagements
  2. Capturing feedback from internal clients on AI outputs
  3. Tracking influence beyond compliance checklist completion
  4. Documenting time saved by standardized AI artefacts
  5. Sharing templates without compromising sensitivity
  6. Recognizing team contributions in AI governance work
  7. Building reputation through consistent quality
  8. Using success stories in internal performance reviews
  9. Aligning with leadership priorities on AI adoption
  10. Creating visibility without over-promotion
  11. Measuring repeat request rates by client group
  12. Case: One practitioner became default for three lines
Module 10. Regulator-Facing AI Documentation That Stands Up
Produce evidence that satisfies reviewer questions without inviting deeper scrutiny.
12 chapters in this module
  1. Anticipating regulator questions on AI in underwriting
  2. Structuring responses to avoid open-ended follow-ups
  3. Using consistent terminology across submissions
  4. Documenting model validation processes for examiners
  5. Preparing for requests on training data composition
  6. Handling questions about third-party AI model oversight
  7. Avoiding overstatement of AI system capabilities
  8. Creating executive summaries from technical artefacts
  9. Timing documentation readiness with regulatory cycles
  10. Leveraging ISO 42001 for jurisdictional consistency
  11. Case: Passing EBA review with minimal follow-up
  12. Post-review process refinement based on feedback
Module 11. Sustaining AI Governance Through Leadership Changes
Institutionalize practices so they endure beyond individual sponsors.
12 chapters in this module
  1. Documenting rationale behind key control decisions
  2. Creating onboarding materials for new compliance leads
  3. Integrating AI governance into standard operating procedures
  4. Using templates to maintain consistency across teams
  5. Archiving decisions in searchable knowledge bases
  6. Linking to corporate policy documents for stability
  7. Training others to maintain the AI register
  8. Establishing routine review cycles for SoA updates
  9. Measuring compliance maturity over time
  10. Preserving lessons from past auditor interactions
  11. Ensuring playbook survives budget cycle shifts
  12. Case: Maintaining AI controls after two manager changes
Module 12. Your Tailored AI Governance Implementation Playbook
Receive a customised, ready-to-use playbook based on your role and environment.
12 chapters in this module
  1. How this playbook was built for insurance contexts
  2. Customizing templates for CGI project deployments
  3. Getting started: First three actions to take
  4. Adapting for single-project vs. multi-client use
  5. Integrating with existing CGI compliance workflows
  6. Updating for changing AI regulations and standards
  7. Sharing selectively with internal teams
  8. Securing playbook in compliance documentation system
  9. Tracking adoption across peer practitioners
  10. Measuring time saved in documentation cycles
  11. Providing feedback for future updates
  12. Next steps: From playbook to sustained practice

How this maps to your situation

  • Insurance-specific AI governance under ISO 42001
  • Regulator-facing documentation workflows
  • Cross-functional escalation management
  • Sustainable compliance in project-based environments

Before vs. after

Before
Reactive, ad hoc responses to AI governance demands, reliance on memory, inconsistent documentation, frequent escalations from peers
After
Proactive ownership of AI compliance workflows, standardized artefacts, reduced review cycles, trusted escalation resolver, documented processes that persist

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: 90 minutes total, designed for completion in one focused session

If nothing changes
Without a structured approach, AI governance work remains reactive , leading to repeated rework, missed influence opportunities, and dependency on individual contributors who rotate off projects.

How this compares to the alternatives

Generic AI ethics courses lack implementation depth. Internal training moves too slowly. This course delivers specific, field-tested workflows for insurance practitioners , ready to use immediately.

Frequently asked

Is this course relevant if I'm not in a leadership role?
Yes , it’s designed for hands-on practitioners who own deliverables, not formal leaders.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work for non-European insurers?
Yes , the implementation playbook addresses multi-jurisdictional needs and global standards alignment.
$199 one-time. 90 minutes total, designed for completion in one focused session.

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
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