A tailored course, built for your situation
Operationally-Sound AI Compliance for Financial Services for Audit Teams
A 12-module implementation-grade course for audit, risk, and compliance professionals advancing AI governance in financial services
$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.
Audit teams face increasing pressure to validate complex AI systems without clear, actionable frameworks.
The situation this course is for
AI adoption in financial services is accelerating, but audit functions often lack structured, repeatable methods to assess model fairness, explainability, and regulatory alignment. Without standardized approaches, audits become inconsistent, time-intensive, and prone to oversight, jeopardizing trust and regulatory standing.
Who this is for
Audit, risk, and compliance professionals in financial services seeking to build credible, repeatable AI assessment capabilities
Who this is not for
This course is not for data scientists building models or executives seeking high-level AI strategy overviews.
What you walk away with
- Apply a structured framework to audit AI systems for compliance and operational soundness
- Evaluate model risk using financial services-specific criteria
- Generate audit-ready documentation for AI deployments
- Implement bias detection and mitigation protocols in practice
- Align AI audits with evolving regulatory expectations
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Compliance in Financial Services
Introduce core concepts of AI governance, regulatory landscape, and audit relevance in financial contexts.
12 chapters in this module
- Understanding AI in regulated finance
- Key regulatory bodies and expectations
- Differences between traditional and AI audits
- Roles of audit, risk, and compliance teams
- Defining operational soundness
- Principles of fairness, accountability, transparency
- Overview of model lifecycle
- Risk categories in AI systems
- Audit boundaries for AI deployments
- Documentation standards and expectations
- Stakeholder alignment in audits
- Course navigation and tools overview
Module 2. Model Risk Management Frameworks
Explore established and emerging frameworks for managing AI model risk from an audit perspective.
12 chapters in this module
- Introduction to model risk
- SR 11-7 and its evolution
- Adapting MRM for AI systems
- Pre-deployment risk assessment
- Ongoing monitoring requirements
- Model inventory and registry design
- Risk tiering and prioritization
- Validation independence and rigor
- Audit scope definition by risk level
- Handling model updates and retraining
- Third-party model oversight
- Reporting model risk to leadership
Module 3. Regulatory Alignment and Expectations
Map AI audit practices to current regulatory guidance across jurisdictions and agencies.
12 chapters in this module
- Global regulatory trends in AI
- U.S. federal and state expectations
- EU AI Act implications for finance
- Cross-border data and model use
- Consumer protection and fair lending
- Anti-discrimination standards
- Data privacy and AI processing
- Enforcement actions and lessons learned
- Regulatory sandboxes and innovation
- Engaging regulators on AI audits
- Proactive compliance posture
- Future-looking regulatory signals
Module 4. Audit Planning for AI Systems
Design audit plans tailored to AI systems, including scope, methodology, and resource allocation.
12 chapters in this module
- Defining audit objectives for AI
- Identifying system boundaries
- Stakeholder interviews and discovery
- Data lineage and provenance mapping
- Algorithmic transparency assessment
- Selecting audit samples and test cases
- Risk-based audit scheduling
- Resource and skill requirements
- Third-party audit coordination
- Documentation of planning phase
- Reviewing model development artifacts
- Setting success criteria
Module 5. Bias Detection and Fairness Testing
Implement practical techniques to detect, measure, and report bias in AI models used in financial services.
12 chapters in this module
- Understanding algorithmic bias
- Sources of bias in training data
- Protected attributes and proxies
- Statistical fairness metrics
- Disparate impact analysis
- Counterfactual fairness testing
- Pre-processing bias mitigation
- In-model fairness constraints
- Post-processing adjustments
- Reporting bias findings to stakeholders
- Remediation pathways
- Ongoing fairness monitoring
Module 6. Explainability and Interpretability
Evaluate and audit AI model outputs using explainability techniques appropriate for financial decision-making.
12 chapters in this module
- Why explainability matters in finance
- Global regulatory expectations
- Local vs. global explanations
- SHAP, LIME, and other methods
- Surrogate models for interpretation
- Feature importance analysis
- Model cards and datasheets
- Audit trails for model reasoning
- Handling black-box models
- Customer-facing explanations
- Documentation of explainability efforts
- Limits of current techniques
Module 7. Data Governance and Provenance
Audit data pipelines for integrity, quality, and compliance across the AI lifecycle.
12 chapters in this module
- Data lifecycle in AI systems
- Data quality assessment frameworks
- Data lineage tracking methods
- Training vs. production data drift
- Data access controls and logging
- Consent and data rights compliance
- Handling sensitive financial data
- Synthetic data and its audit implications
- Versioning and reproducibility
- Data retention and deletion
- Third-party data sourcing
- Audit evidence collection for data
Module 8. Model Validation and Testing
Conduct independent validation of AI models using audit-appropriate techniques and documentation.
12 chapters in this module
- Principles of model validation
- Independence and objectivity standards
- Backtesting and stress testing
- Performance metric selection
- Threshold stability analysis
- Edge case identification
- Scenario testing for rare events
- Adversarial testing techniques
- Reproducibility of results
- Validation documentation standards
- Handling model degradation
- Re-validation triggers
Module 9. Audit Trail Design and Integrity
Assess and improve audit trail systems to ensure completeness, immutability, and accessibility.
12 chapters in this module
- Core components of AI audit trails
- Event logging standards
- Immutable logging solutions
- Timestamping and sequence integrity
- Access and change logs
- Metadata capture requirements
- Chain of custody for models
- Integration with existing GRC tools
- Automated alerting on anomalies
- Retention and archiving policies
- Regulatory inspection readiness
- Testing trail completeness
Module 10. Third-Party and Vendor AI Oversight
Audit AI systems developed or managed by external vendors with appropriate rigor and transparency.
12 chapters in this module
- Risks of third-party AI models
- Vendor due diligence process
- Contractual audit rights
- Access to source code and data
- Model documentation requirements
- Performance benchmarking
- Ongoing monitoring of vendors
- Incident response coordination
- Exit strategies and model portability
- Sub-vendor oversight
- Regulatory reporting for vendor models
- Managing conflicts of interest
Module 11. Documentation and Reporting Standards
Produce clear, comprehensive, and regulator-ready audit documentation for AI systems.
12 chapters in this module
- Structure of AI audit reports
- Executive summary best practices
- Technical findings and evidence
- Risk rating methodologies
- Remediation recommendations
- Appendices and supporting data
- Version control for reports
- Internal distribution protocols
- Regulatory submission formats
- Stakeholder communication strategies
- Lessons learned documentation
- Knowledge transfer to teams
Module 12. Scaling AI Audit Practices
Build sustainable, repeatable processes to scale AI audit capabilities across the organization.
12 chapters in this module
- From project to program: scaling strategy
- Center of excellence models
- Training internal teams
- Standardizing templates and tools
- Integrating with existing audit workflows
- Automation opportunities
- Metrics for audit effectiveness
- Continuous improvement cycles
- Cross-functional collaboration
- Leadership reporting and updates
- Budgeting and resourcing
- Future trends in AI auditing
How this maps to your situation
- Auditing credit scoring models
- Validating fraud detection systems
- Reviewing customer service chatbots
- Assessing portfolio risk models
Before vs. after
Before
Audit teams navigate AI systems with fragmented guidance, inconsistent methods, and limited documentation support.
After
Audit teams apply a structured, repeatable, and regulator-aligned approach to assess AI systems with confidence and clarity.
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 45, 60 hours total, designed for self-paced learning with practical application between modules.
If nothing changes
Without structured AI audit practices, organizations risk inconsistent evaluations, regulatory scrutiny, and reputational harm due to undetected model issues.
How this compares to the alternatives
Unlike high-level overviews or technical model-building courses, this program focuses exclusively on audit-grade implementation for compliance professionals in financial services, offering structured workflows, templates, and regulatory alignment not found in generic AI ethics or data science training.
Frequently asked
Who is this course designed for?
Audit, risk, and compliance professionals in financial services who need to assess AI systems with operational rigor and regulatory alignment.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or conceptual?
It is implementation-grade, practical and detailed, designed for professionals who need to apply frameworks, not just understand concepts.
$199 one-time. Approximately 45, 60 hours total, designed for self-paced learning with practical application between modules..
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