A tailored course, built for your situation
Cross-Functional AI Compliance for Financial Services
Implementation-grade mastery for regulated industry professionals
$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.
AI initiatives stall when compliance, risk, and technology teams work in silos
The situation this course is for
Even well-resourced teams struggle to align AI innovation with regulatory expectations. Without a shared framework, projects face delays, rework, or rejection at review stages. The gap isn’t technical, it’s operational and cultural.
Who this is for
Mid-to-senior level professionals in compliance, risk, governance, data science, or technology roles within regulated financial institutions who are accountable for AI system oversight and implementation
Who this is not for
Individuals seeking introductory AI concepts or general data privacy training without a focus on financial services regulation
What you walk away with
- Lead cross-functional AI compliance initiatives with confidence
- Apply a structured framework that satisfies both technical and regulatory requirements
- Reduce time-to-approval for AI projects using standardized documentation
- Anticipate regulatory expectations across jurisdictions and use cases
- Implement repeatable processes for model validation, monitoring, and audit readiness
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Governance in Financial Services
Establish core principles, regulatory touchpoints, and cross-functional roles
12 chapters in this module
- Defining AI in regulated contexts
- Global regulatory landscape overview
- Key frameworks: Basel, IOSCO, FATF
- RegTech and GovAI convergence
- Governance vs. compliance distinctions
- Board oversight expectations
- Risk appetite framework integration
- Stakeholder mapping
- Cross-functional team charters
- Operating model alignment
- Accountability frameworks
- Documentation standards
Module 2. Regulatory Expectations for AI Systems
Decode expectations from major financial regulators
12 chapters in this module
- Prudential standards for AI use
- Conduct risk and AI interactions
- Fair lending implications
- Model risk management evolution
- SR 11-7 applicability
- Enforcement case analysis
- Supervisory college insights
- Guidance from central banks
- Cross-border considerations
- AI-specific regulatory sandboxes
- Disclosure requirements
- Regulator communication protocols
Module 3. Cross-Functional Team Design
Architect teams that bridge silos
12 chapters in this module
- RACI matrix for AI projects
- Compliance integration patterns
- Legal department engagement
- Risk management collaboration
- IT and security alignment
- Data governance partnerships
- Product and engineering coordination
- Third-party vendor oversight
- External auditor preparation
- Change management integration
- Training and enablement plans
- Performance metrics alignment
Module 4. AI Lifecycle Compliance Mapping
Align each phase with regulatory requirements
12 chapters in this module
- Idea intake and screening
- Feasibility and risk assessment
- Data sourcing compliance
- Model development controls
- Validation independence
- Testing protocols
- Implementation safeguards
- Monitoring thresholds
- Drift detection standards
- Remediation workflows
- Decommissioning process
- Audit trail maintenance
Module 5. Model Risk Management Integration
Adapt MRAs for AI-specific risks
12 chapters in this module
- Model inventory classification
- Risk tiering methodology
- Validation scope determination
- Challenge process design
- Ongoing monitoring KPIs
- Backtesting requirements
- Performance degradation signals
- Model drift response
- Version control compliance
- Retraining triggers
- Model lineage tracking
- Documentation completeness
Module 6. Explainability and Fairness Engineering
Operationalize ethical AI principles
12 chapters in this module
- Regulatory expectations for explainability
- Technical methods for interpretability
- SHAP, LIME, and counterfactuals
- Bias detection frameworks
- Fairness metrics selection
- Disparate impact testing
- Red teaming procedures
- Human-in-the-loop design
- Decision logging standards
- Appeal process integration
- Transparency reporting
- Customer communication protocols
Module 7. Data Governance for AI Systems
Ensure data quality and provenance
12 chapters in this module
- Data lineage requirements
- Training vs. inference data
- Bias in data sources
- Data quality metrics
- Sensitive data handling
- Consent management alignment
- Third-party data validation
- Synthetic data governance
- Data retention policies
- Audit readiness for data
- Data versioning standards
- Data drift monitoring
Module 8. AI System Documentation Standards
Create regulator-ready artifacts
12 chapters in this module
- Model development dossier
- Validation report structure
- Governance committee minutes
- Risk assessment templates
- Control environment documentation
- Model performance dashboards
- Incident reporting logs
- Change request tracking
- Vendor oversight records
- Compliance attestations
- Audit preparation packages
- Board reporting materials
Module 9. Monitoring and Incident Response
Detect and respond to AI issues
12 chapters in this module
- Performance threshold setting
- Drift detection methods
- Anomaly escalation paths
- Incident classification
- Root cause analysis
- Remediation planning
- Regulatory reporting triggers
- Customer impact assessment
- Recovery procedures
- Post-mortem frameworks
- Trend analysis
- Lessons learned integration
Module 10. Third-Party and Vendor Oversight
Extend compliance to external partners
12 chapters in this module
- Vendor due diligence
- Contractual requirements
- Audit rights negotiation
- Subcontractor oversight
- Model validation independence
- Data protection clauses
- Exit strategy planning
- Performance monitoring
- Compliance certification
- Incident response coordination
- Knowledge transfer requirements
- Vendor management reporting
Module 11. Regulatory Examination Readiness
Prepare for supervisory review
12 chapters in this module
- Examination scope anticipation
- Document organization
- Interview preparation
- Response protocols
- Deficiency tracking
- Remediation planning
- Regulator communication
- Evidence collection
- Gap assessment methods
- Mock examination
- Follow-up procedures
- Continuous improvement
Module 12. Scaling AI Compliance Across the Enterprise
Build repeatable, organization-wide practices
12 chapters in this module
- Center of excellence design
- Playbook standardization
- Training curriculum development
- Automation opportunities
- Tooling integration
- Knowledge management
- Metrics and reporting
- Continuous monitoring
- Innovation enablement
- Lessons learned integration
- Benchmarking against peers
- Future readiness planning
How this maps to your situation
- New AI initiative launch
- Regulatory examination preparation
- Cross-team alignment challenge
- Post-incident review and improvement
Before vs. after
Before
AI projects face delays due to unclear compliance expectations and misaligned teams
After
Cross-functional teams move faster with shared frameworks, standardized documentation, and regulator-ready processes
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, 50 hours of self-paced learning, designed for professionals balancing delivery responsibilities
If nothing changes
Without structured AI compliance practices, organizations risk project delays, regulatory scrutiny, and erosion of stakeholder trust, especially as board-level expectations continue to rise
How this compares to the alternatives
Unlike generic AI ethics courses or high-level overviews, this program delivers implementation-grade content specific to financial services regulation, with templates and playbooks you can apply immediately
Frequently asked
Who is this course designed for?
It's for compliance, risk, governance, and technology professionals in regulated financial institutions who need to implement AI systems responsibly.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course jurisdiction-specific?
It covers global regulatory expectations and is applicable across major financial centers and supervisory regimes.
$199 one-time. Approximately 40, 50 hours of self-paced learning, designed for professionals balancing delivery responsibilities.
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