A tailored course, built for your situation
Production-Grade AI Compliance for Financial Services for Audit Teams
A 12-module implementation framework for audit, risk, and technology 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.
Audit teams face increasing pressure to validate AI systems without clear, actionable compliance 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 governance, data provenance, and regulatory alignment. This creates delays, rework, and uncertainty during reviews.
Who this is for
Compliance officers, internal auditors, risk managers, and technology leads in financial institutions implementing or scaling AI systems.
Who this is not for
This course is not for data scientists focused only on model development, or executives seeking high-level overviews without implementation detail.
What you walk away with
- Apply a standardized framework to audit AI systems across the lifecycle
- Map AI controls to financial services regulations and internal policies
- Build compliant model documentation and audit trails
- Deploy repeatable review processes using templates and checklists
- Lead cross-functional alignment between tech, risk, and audit teams
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Compliance in Financial Services
Establish core principles, regulatory expectations, and audit relevance.
12 chapters in this module
- Introduction to AI in regulated financial environments
- Key regulatory bodies and their AI guidance
- Audit’s role in AI governance
- Risk categories in AI-driven financial products
- Compliance maturity models
- Case study: Credit scoring system review
- Defining 'production-grade' compliance
- Stakeholder alignment frameworks
- Regulatory trend analysis techniques
- Documentation standards for auditable AI
- Common gaps in AI compliance programs
- Building a compliance-first culture
Module 2. Model Risk Management Frameworks
Implement structured risk assessment for AI models.
12 chapters in this module
- Model risk taxonomy in financial services
- Pre-deployment risk scoring
- Ongoing monitoring triggers
- Model inventory design
- Risk rating calibration
- Third-party model risk
- Model decay detection
- Scenario testing for edge cases
- Risk escalation protocols
- Audit evidence requirements
- Model retirement compliance
- Integration with enterprise risk management
Module 3. Regulatory Mapping and Alignment
Connect AI controls to specific regulatory requirements.
12 chapters in this module
- Mapping AI practices to GLBA, FCRA, and ECOA
- Fair lending implications of AI models
- CCPA and data use transparency
- SEC expectations for AI disclosures
- FDIC and OCC guidance on algorithmic risk
- Cross-border regulatory considerations
- Regulatory change tracking systems
- Gap analysis methodology
- Evidence packages for regulators
- Audit response preparation
- Regulatory inspection simulations
- Maintaining alignment over time
Module 4. Audit Trail Design for AI Systems
Create tamper-resistant, auditable records of AI behavior.
12 chapters in this module
- Components of an AI audit trail
- Data lineage tracking
- Model version provenance
- Input-output logging standards
- Decision rationale capture
- User interaction logging
- Immutable storage options
- Access controls for audit data
- Automated anomaly detection in logs
- Log retention and purge policies
- Audit trail validation techniques
- Preparing logs for regulatory review
Module 5. Bias Detection and Fairness Testing
Operationalize fairness assessments in model audits.
12 chapters in this module
- Defining fairness in financial contexts
- Bias detection across demographic segments
- Statistical fairness metrics
- Disparate impact analysis
- Pre-processing bias mitigation
- In-model fairness constraints
- Post-hoc adjustment evaluation
- Fairness testing automation
- Documentation of bias assessments
- Stakeholder communication of findings
- Remediation planning
- Ongoing fairness monitoring
Module 6. Explainability and Interpretability Standards
Ensure AI decisions can be understood and justified.
12 chapters in this module
- Types of model explainability
- SHAP, LIME, and other interpretability tools
- Regulatory expectations for explainability
- Customer-facing explanation requirements
- Audit-ready explanation packages
- Trade-offs between accuracy and explainability
- Simplified explanations for non-technical reviewers
- Model cards and fact sheets
- Explainability in real-time systems
- Third-party model transparency
- Documentation templates
- Validation of explanation outputs
Module 7. Data Governance for AI Compliance
Establish compliant data practices supporting AI systems.
12 chapters in this module
- Data provenance and chain of custody
- Consent management for training data
- Data quality assurance protocols
- Sensitive data handling in AI
- Data retention and deletion compliance
- Data minimization in model design
- Cross-border data transfer rules
- Vendor data governance oversight
- Data inventory for AI systems
- Audit evidence for data practices
- Automated data policy enforcement
- Data governance maturity assessment
Module 8. Model Validation and Testing Protocols
Design rigorous validation processes for AI models.
12 chapters in this module
- Phases of model validation
- Independent validation requirements
- Backtesting methodologies
- Stress testing AI models
- Benchmarking against alternatives
- Sensitivity analysis techniques
- Edge case identification
- Failure mode analysis
- Validation documentation standards
- Third-party validation coordination
- Ongoing validation cycles
- Audit readiness for validation artifacts
Module 9. Change Management and Version Control
Govern AI model updates and deployments.
12 chapters in this module
- AI change control processes
- Version control for models and data
- Change impact assessments
- Approval workflows for model updates
- Rollback procedures
- Communication of model changes
- Audit trail updates for changes
- Regression testing requirements
- Stakeholder notification protocols
- Change audit readiness
- Automated change detection
- Incident response for unauthorized changes
Module 10. Third-Party and Vendor AI Oversight
Extend compliance to external AI providers.
12 chapters in this module
- Vendor risk assessment for AI tools
- Contractual compliance requirements
- Third-party audit rights
- Model transparency from vendors
- Data handling in vendor systems
- Ongoing vendor monitoring
- Subprocessor oversight
- Vendor incident response coordination
- Due diligence checklists
- Audit evidence from vendors
- Exit strategy compliance
- Vendor consolidation strategies
Module 11. Cross-Functional Collaboration Frameworks
Align audit, risk, legal, and technology teams.
12 chapters in this module
- RACI matrices for AI governance
- Interdepartmental communication protocols
- Joint risk assessment sessions
- Shared documentation platforms
- Conflict resolution in AI reviews
- Executive reporting alignment
- Training for cross-functional teams
- Feedback loops between audit and development
- Escalation pathways
- Metrics for collaboration effectiveness
- Cultural barriers to alignment
- Sustaining collaboration over time
Module 12. Scaling AI Compliance Across the Enterprise
Expand compliance practices to multiple models and teams.
12 chapters in this module
- Centralized vs decentralized governance
- AI compliance center of excellence
- Automated compliance tooling
- Standardized templates and playbooks
- Training programs for auditors
- Compliance metrics and KPIs
- Lessons from early adopters
- Technology stack integration
- Continuous improvement cycles
- Board-level reporting frameworks
- Regulatory engagement strategy
- Future-proofing the compliance function
How this maps to your situation
- Auditing AI systems in loan underwriting
- Validating customer service chatbots for compliance
- Reviewing fraud detection models
- Assessing third-party credit risk scoring tools
Before vs. after
Before
Audit teams work reactively, scrambling to assess AI systems without standardized methods or reusable tools.
After
Audit functions operate proactively with a structured, repeatable, and regulator-ready framework for AI compliance.
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 flexible, self-paced completion over 6, 8 weeks.
If nothing changes
Without structured AI compliance practices, audit teams risk delays, inconsistent reviews, regulatory scrutiny, and erosion of trust in AI systems.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementation-grade detail tailored to financial services audit teams, with actionable templates and a custom playbook.
Frequently asked
Who is this course designed for?
Compliance officers, internal auditors, risk managers, and technology leads in financial institutions implementing or scaling AI systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, a 30-day money-back guarantee is included.
$199 one-time. Approximately 45, 60 hours total, designed for flexible, self-paced completion over 6, 8 weeks..
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