A tailored course, built for your situation
Board-Level AI Compliance for Financial Services
A cross-functional implementation blueprint for governance, risk, and technology leaders
$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.
Complex AI initiatives stall without clear compliance pathways across legal, risk, and technical teams
The situation this course is for
AI governance remains siloed, legal sees risk, tech sees innovation, and leadership sees uncertainty. Without a shared framework, initiatives face delays, rework, or abandonment at critical stages. Practitioners lack structured methods to align stakeholders, demonstrate regulatory readiness, and maintain momentum across departments.
Who this is for
Mid-to-senior level professionals in compliance, risk, technology, data governance, or internal audit roles within financial institutions leading or supporting AI-driven programs
Who this is not for
Entry-level analysts, purely technical developers without governance responsibilities, or executives seeking only high-level overviews without implementation detail
What you walk away with
- Lead AI compliance initiatives with confidence across legal, risk, and technology functions
- Apply a structured governance framework aligned with global financial regulations
- Translate board-level expectations into operational action plans
- Navigate audit and regulatory scrutiny with prepared documentation and controls
- Drive cross-functional alignment using shared playbooks and decision templates
The 12 modules (with all 144 chapters)
Module 1. AI Governance in Financial Services
Foundations of AI compliance at the board level
12 chapters in this module
- Defining AI compliance in regulated finance
- Roles of board and executive oversight
- Regulatory drivers shaping AI policy
- Global trends in AI supervision
- Key standards and frameworks
- Differences between AI ethics and compliance
- Case study: AI rollout at a Tier 1 bank
- Stakeholder map: who decides what
- Risk taxonomy for AI systems
- Compliance lifecycle overview
- Integration with enterprise risk management
- Common failure points in governance
Module 2. Regulatory Landscape and Expectations
Mapping current and emerging requirements
12 chapters in this module
- Jurisdictional variation in AI rules
- APAC regulatory posture on AI use
- EU AI Act implications for finance
- US regulatory guidance and enforcement
- ASIC and RBA expectations
- Cross-border data and model use
- Model validation requirements
- Consumer protection and fairness
- Recordkeeping and transparency rules
- Audit trail expectations
- Licensing implications for AI tools
- Regulatory sandboxes and testing
Module 3. Cross-Functional Program Design
Structuring teams and workflows for compliance
12 chapters in this module
- Defining cross-functional roles
- Governance committee structures
- RACI matrix for AI projects
- Handoff protocols between teams
- Change management for AI rollout
- Communication frameworks for boards
- Escalation pathways for risk issues
- Resource planning for compliance
- Vendor and third-party oversight
- Training programs for staff
- KPIs for program success
- Post-implementation review design
Module 4. Model Risk Management Frameworks
Applying MRMs to AI and machine learning
12 chapters in this module
- Extending MRM to AI models
- Model inventory and cataloging
- Model validation techniques
- Performance monitoring standards
- Bias detection and mitigation
- Explainability requirements
- Model documentation standards
- Model lifecycle controls
- Independent review expectations
- Model revalidation triggers
- Model decommissioning
- AI-specific risk indicators
Module 5. Audit and Regulatory Readiness
Preparing for internal and external scrutiny
12 chapters in this module
- Audit scope for AI systems
- Internal audit coordination
- Regulatory examination preparation
- Document packet assembly
- Evidence retention standards
- Response protocols to findings
- Corrective action planning
- Compliance testing frameworks
- Gap assessment methodologies
- Third-party audit support
- Regulatory inquiry handling
- Lessons from past enforcement cases
Module 6. Ethical AI and Fairness Controls
Embedding fairness and accountability
12 chapters in this module
- Defining fairness in financial contexts
- Bias sources in training data
- Algorithmic impact assessments
- Fair lending considerations
- Disparate impact testing
- Redress mechanisms for customers
- Human-in-the-loop design
- Transparency vs. explainability
- Customer communication standards
- Ongoing fairness monitoring
- Ethics review board models
- Public trust and brand impact
Module 7. Data Governance for AI Systems
Ensuring data quality and compliance
12 chapters in this module
- Data provenance and lineage
- Data quality benchmarks
- Consent and usage rights
- PII handling in AI workflows
- Data retention policies
- Cross-border data transfer rules
- Data access controls
- Data drift detection
- Synthetic data use cases
- Data labeling standards
- Vendor data compliance
- Data audit readiness
Module 8. Incident Response and Model Monitoring
Detecting and managing AI issues
12 chapters in this module
- Model performance thresholds
- Anomaly detection in predictions
- Drift monitoring systems
- Incident classification levels
- Response team activation
- Root cause analysis for AI errors
- Customer impact assessment
- Regulatory breach protocols
- Public disclosure considerations
- Model rollback procedures
- Post-mortem documentation
- Lessons learned integration
Module 9. Board Reporting and Executive Communication
Translating technical details for leadership
12 chapters in this module
- Board-level reporting cadence
- Risk dashboard design
- Executive summary standards
- Key risk indicators for AI
- Escalation thresholds
- Scenario planning for AI risk
- Crisis communication templates
- Regulatory update briefings
- Budget justification for compliance
- Success story reporting
- Benchmarking against peers
- Strategic opportunity framing
Module 10. Vendor and Third-Party Oversight
Managing external AI providers
12 chapters in this module
- Due diligence for AI vendors
- Contractual compliance clauses
- Right-to-audit provisions
- Model ownership and IP
- Subcontractor oversight
- Cloud provider responsibilities
- API security and monitoring
- Service level agreements
- Performance benchmarking
- Exit strategy planning
- Vendor risk tiering
- Ongoing monitoring protocols
Module 11. Implementation Playbook Integration
Applying course tools in real programs
12 chapters in this module
- Customizing templates to context
- Stakeholder alignment workshop design
- Pilot project selection
- Change request workflows
- Compliance checklist integration
- Document repository setup
- Training rollout planning
- Feedback loop mechanisms
- Compliance maturity assessment
- Scaling from pilot to enterprise
- Lessons from early adopters
- Continuous improvement cycle
Module 12. Future-Proofing and Evolving Standards
Anticipating next-phase requirements
12 chapters in this module
- Signals of regulatory evolution
- Global coordination trends
- Emerging technical standards
- AI insurance and liability
- Cybersecurity convergence
- Climate risk and AI linkage
- Workforce implications
- AI audit certification paths
- Public-private collaboration
- Scenario planning for regulation
- Advocacy and industry influence
- Lifelong learning for practitioners
How this maps to your situation
- Regulatory scrutiny intensification
- Cross-departmental friction in AI rollout
- Board demand for AI risk transparency
- Audit preparation for AI systems
Before vs. after
Before
AI compliance feels reactive, fragmented, and dependent on tribal knowledge
After
You lead with a structured, repeatable approach that aligns technology, risk, and business teams under shared governance
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 40 hours total, designed for flexible engagement across 6-8 weeks with full access for 12 months.
If nothing changes
Without structured AI compliance practices, organizations face delayed initiatives, regulatory friction, and reputational exposure, even when intent and technology are sound. The gap isn't innovation capacity; it's implementation clarity.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level overviews, this program delivers implementation-grade structure for financial services compliance. It goes beyond theory with field-tested templates, regulatory mappings, and cross-functional coordination playbooks not found in public or vendor training.
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, technology leads, data governance professionals, and internal auditors in financial institutions who are involved in AI-driven programs and need to ensure board-level alignment and regulatory readiness.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a certificate is issued upon finishing all modules and passing the final assessment.
$199 one-time. Approximately 40 hours total, designed for flexible engagement across 6-8 weeks with full access for 12 months..
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