A tailored course, built for your situation
Audit-Tested AI Compliance for Financial Services
Implement AI systems with confidence in regulated 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.
Deploying AI in financial services without audit-ready compliance creates execution risk and delays
The situation this course is for
Teams are under pressure to deliver AI-driven solutions quickly, but face growing scrutiny from regulators and internal auditors. Without a structured, audit-tested approach, even well-designed models stall in governance review or fail validation, wasting time, resources, and strategic momentum.
Who this is for
Business and technology professionals in financial services and regulated industries responsible for AI governance, risk management, compliance, or technical implementation
Who this is not for
This course is not for academic researchers, hobbyists, or professionals in unregulated consumer tech sectors without compliance mandates
What you walk away with
- Design AI systems that meet current regulatory expectations
- Prepare audit-ready documentation for model development and deployment
- Implement governance controls that satisfy internal and external reviewers
- Navigate model validation requirements across jurisdictions
- Lead cross-functional teams with confidence in compliance frameworks
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Compliance in Financial Services
Establish core principles of responsible AI in regulated environments
12 chapters in this module
- Defining audit-tested AI
- Regulatory landscape overview
- Key standards and frameworks
- Role of governance bodies
- Risk categorization models
- Compliance by design principles
- Stakeholder mapping
- Internal policy alignment
- Audit lifecycle basics
- Documentation expectations
- Model inventory management
- Compliance maturity assessment
Module 2. Regulatory Expectations and Jurisdictional Alignment
Understand global and regional requirements for AI in finance
12 chapters in this module
- Evolving regulatory priorities
- U.S. federal and state guidance
- EU AI Act implications
- UK FCA and PRA expectations
- APAC regulatory approaches
- Cross-border data flows
- Harmonizing multi-jurisdictional rules
- Engaging with regulators
- Interpreting enforcement trends
- Supervisory expectations
- Regulatory sandbox participation
- Future-looking compliance planning
Module 3. Model Risk Management Frameworks
Adapt traditional MRMs for AI-driven systems
12 chapters in this module
- Extending SR 11-7 to AI
- Model classification tiers
- Pre-development risk assessment
- Development lifecycle controls
- Versioning and change tracking
- Model performance thresholds
- Ongoing monitoring requirements
- Retirement and decommissioning
- Independent validation roles
- Escalation protocols
- Third-party model oversight
- Audit trail preservation
Module 4. AI Governance Structures and Accountability
Build effective oversight models for AI initiatives
12 chapters in this module
- AI governance committee design
- Clear role definitions (RACI)
- Escalation pathways
- Board-level reporting
- Ethics review integration
- Conflict resolution mechanisms
- Decision logging
- Training and awareness programs
- Policy enforcement tracking
- Third-party governance
- Vendor risk integration
- Continuous improvement cycles
Module 5. Data Provenance and Integrity Controls
Ensure data quality and lineage for audit readiness
12 chapters in this module
- Data sourcing standards
- Bias detection in training data
- Data quality metrics
- Lineage tracking systems
- Anonymization and privacy safeguards
- Data access controls
- Retention and deletion policies
- Synthetic data governance
- External data validation
- Data drift monitoring
- Audit log requirements
- Chain of custody documentation
Module 6. Algorithmic Transparency and Explainability
Meet explainability demands without sacrificing performance
12 chapters in this module
- Types of model interpretability
- SHAP and LIME applications
- Surrogate modeling
- Local vs global explanations
- Consumer-facing disclosures
- Regulator reporting formats
- Trade-offs with model complexity
- Documentation templates
- User testing of explanations
- Model cards and datasheets
- Explainability in credit decisions
- Handling black-box models
Module 7. Bias Detection and Fairness Testing
Implement robust fairness assessments across the lifecycle
12 chapters in this module
- Defining fairness metrics
- Protected attribute handling
- Disparate impact analysis
- Pre-processing mitigation
- In-model fairness constraints
- Post-processing adjustments
- Segmented performance evaluation
- Fairness in NLP systems
- Bias testing automation
- Audit evidence packaging
- Third-party fairness audits
- Remediation workflows
Module 8. Model Validation and Independent Review
Conduct validation that satisfies internal and external auditors
12 chapters in this module
- Validation team independence
- Test plan development
- Backtesting methodologies
- Sensitivity analysis
- Stress testing scenarios
- Benchmarking against alternatives
- Code review protocols
- Documentation completeness checks
- Edge case evaluation
- Performance decay detection
- Validation report templates
- Follow-up on findings
Module 9. Documentation Standards for Audits
Create comprehensive, auditor-friendly records
12 chapters in this module
- Model development dossier
- Version-controlled documentation
- Change request logs
- Assumption tracking
- Risk control matrices
- Validation evidence packages
- Stakeholder approval records
- Meeting minutes and decisions
- Regulatory correspondence
- Gap remediation tracking
- Document retention policies
- Audit response preparation
Module 10. Operational Monitoring and Incident Response
Maintain compliance during live model operation
12 chapters in this module
- Real-time performance dashboards
- Drift detection systems
- Threshold alerting
- Incident classification
- Root cause analysis
- Remediation playbooks
- Escalation procedures
- Model retraining triggers
- Outage communication plans
- Regulatory breach reporting
- Post-mortem documentation
- Continuous monitoring audits
Module 11. Third-Party and Vendor AI Oversight
Extend compliance to external AI providers
12 chapters in this module
- Vendor due diligence
- Contractual compliance clauses
- API-level monitoring
- Subprocessor transparency
- Right-to-audit provisions
- Performance SLAs
- Security certification requirements
- Model update notifications
- Vendor risk scoring
- Onboarding checklists
- Exit strategy planning
- Joint incident response
Module 12. Preparing for Regulatory and Internal Audits
Navigate audit processes with confidence and clarity
12 chapters in this module
- Audit readiness checklist
- Evidence organization
- Mock audit exercises
- Interview preparation
- Deficiency response planning
- Regulatory inquiry handling
- Cross-functional coordination
- Document retrieval systems
- Timeline management
- Findings tracking
- Remediation demonstration
- Post-audit review
How this maps to your situation
- Designing a new AI system for regulated use
- Facing internal audit scrutiny on existing models
- Scaling AI initiatives across business units
- Responding to evolving regulatory expectations
Before vs. after
Before
Uncertainty about how to structure AI projects for audit success, leading to delays and rework
After
Confidence in building and documenting AI systems that meet compliance standards from the start
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 4-6 hours per module, recommended over 12 weeks for full implementation integration
If nothing changes
Without a structured approach, AI initiatives risk rejection during governance review, face costly rework, or fail audit, jeopardizing trust, timelines, and strategic goals.
How this compares to the alternatives
Unlike generic AI ethics courses or academic risk management programs, this course delivers actionable, audit-tested frameworks specifically for financial services, aligned with current supervisory expectations and implementation realities.
Frequently asked
Who is this course designed for?
It's for business and technology professionals in financial services and regulated industries who need to implement AI systems that pass internal and external audits.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or strategic?
It balances both, providing technical depth for implementation while maintaining strategic alignment with governance and compliance objectives.
$199 one-time. Approximately 4-6 hours per module, recommended over 12 weeks for full implementation integration.
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