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 and compliance professionals navigating 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 are expected to govern AI systems they weren’t trained to assess.
The situation this course is for
AI adoption in financial services is accelerating, but audit functions lack standardized, practical frameworks to assess model fairness, traceability, and compliance at scale. Traditional review methods don’t map cleanly to dynamic AI systems, creating tension between risk assurance and innovation speed.
Who this is for
Compliance officers, internal auditors, and risk specialists in financial institutions who are responsible for validating AI-driven processes but lack structured, field-tested guidance.
Who this is not for
This course is not for data scientists building AI models or executives seeking high-level overviews. It is also not for professionals outside financial services or those not involved in audit, compliance, or control validation.
What you walk away with
- Apply a structured control framework to AI systems in financial contexts
- Conduct model audits using standardized, repeatable checklists
- Document compliance evidence that meets regulatory expectations
- Align AI governance with existing financial control standards
- Implement an audit-ready playbook for current and future AI deployments
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI in Financial Services
Introduces the role of AI in current financial operations, focusing on risk, compliance, and customer engagement use cases.
12 chapters in this module
- Defining AI in the financial context
- Common AI applications in banking and insurance
- Regulatory drivers shaping AI adoption
- Distinguishing AI from automation
- Lifecycle stages of AI deployment
- Key stakeholders in AI governance
- Risk categories unique to AI systems
- Ethical considerations in financial AI
- Audit relevance of model transparency
- Data provenance and lineage
- Model performance vs. fairness
- Baseline terminology for audit teams
Module 2. Regulatory Landscape for AI in Finance
Covers global standards, supervisory expectations, and enforcement trends relevant to AI compliance.
12 chapters in this module
- Global regulatory frameworks overview
- Evolving guidance from central banks
- Consumer protection and AI
- Anti-discrimination principles in lending models
- Cross-border data and model use
- Enforcement actions and lessons learned
- RegTech responses to AI oversight
- Future-looking supervisory statements
- Interpreting 'prudential' expectations
- Mapping regulations to audit scope
- Handling regulatory ambiguity
- Preparing for AI-specific audits
Module 3. Control Frameworks for AI Systems
Details how to adapt existing financial controls to AI workflows and introduce new governance layers.
12 chapters in this module
- Integrating AI into SOX controls
- Designing for auditability from inception
- Model development lifecycle controls
- Versioning and change management
- Access and authorization for AI systems
- Input data integrity checks
- Output monitoring and anomaly detection
- Human-in-the-loop requirements
- Fallback mechanisms and fail-safes
- Documentation standards for models
- Model risk management alignment
- Third-party AI vendor oversight
Module 4. Model Auditing Fundamentals
Provides a step-by-step approach to auditing AI models, from design to deployment.
12 chapters in this module
- Scoping an AI audit engagement
- Reviewing model design documentation
- Assessing training data representativeness
- Evaluating bias and fairness testing
- Validating model performance metrics
- Testing for concept drift
- Reviewing validation procedures
- Examining model interpretability
- Auditing ensemble models
- Sampling techniques for AI outputs
- Documenting audit findings
- Reporting to audit committees
Module 5. Documentation Standards for AI Compliance
Covers required artifacts, templates, and reporting structures to demonstrate compliance.
12 chapters in this module
- Model documentation inventory
- Creating a model inventory register
- Standardized model cards
- Data lineage documentation
- Bias assessment reports
- Performance monitoring logs
- Change history tracking
- Audit trail requirements
- Version control documentation
- Third-party model documentation
- Internal reporting templates
- Regulatory submission packages
Module 6. Bias and Fairness in Financial AI
Focuses on detecting, measuring, and mitigating bias in credit, underwriting, and servicing models.
12 chapters in this module
- Defining fairness in financial contexts
- Protected attributes in lending
- Disparate impact analysis
- Statistical fairness metrics
- Bias detection in training data
- Bias detection in model outputs
- Pre-processing bias mitigation
- In-model fairness constraints
- Post-processing adjustments
- Monitoring for bias drift
- Reporting bias findings
- Remediation protocols
Module 7. Explainability and Interpretability
Teaches how to assess and report on model transparency for audit and regulatory purposes.
12 chapters in this module
- Why explainability matters for audits
- Types of model interpretability
- Local vs. global explanations
- SHAP and LIME for financial models
- Surrogate models for complex systems
- Feature importance analysis
- Stress-testing model logic
- Documenting explanation methods
- Assessing explanation reliability
- Communicating explanations to non-experts
- Regulatory expectations on transparency
- Audit testing of explanations
Module 8. AI in Credit Risk and Underwriting
Examines AI applications in lending and the associated audit considerations.
12 chapters in this module
- AI in credit scoring models
- Alternative data in underwriting
- Model validation for credit decisions
- Fair lending compliance
- Adverse action notice requirements
- Monitoring for disparate treatment
- Model performance in downturns
- Stress testing AI models
- Human override mechanisms
- Loan origination system integration
- Audit trails for lending decisions
- Regulatory expectations for transparency
Module 9. AI in Fraud Detection and AML
Covers AI use in transaction monitoring and the compliance implications.
12 chapters in this module
- AI in real-time fraud detection
- Behavioral analytics in fraud models
- Model accuracy vs. false positives
- Monitoring model drift
- Explainability in alert generation
- Audit trails for flagged transactions
- Human review of AI alerts
- Calibrating sensitivity thresholds
- Performance metrics for AML models
- Regulatory reporting requirements
- Model validation for AML
- Third-party fraud model oversight
Module 10. AI in Customer Service and Engagement
Explores AI in chatbots, recommendations, and personalization from a compliance lens.
12 chapters in this module
- AI in customer service chatbots
- Personalization engines and bias
- Recommendation fairness
- Consent and data use transparency
- Monitoring for inappropriate responses
- Handling sensitive customer queries
- Audit trails for AI interactions
- Compliance with communication standards
- Regulatory expectations for chatbots
- Documenting customer interaction logic
- Escalation protocols
- Performance measurement
Module 11. Third-Party AI Vendor Management
Guides audit teams on assessing and overseeing external AI providers.
12 chapters in this module
- Vendor due diligence for AI
- Assessing vendor model documentation
- Contractual requirements for audit access
- Right-to-audit clauses
- Data handling and security
- Model performance SLAs
- Transparency obligations
- Change management with vendors
- Incident response coordination
- Exit strategy and data retrieval
- Ongoing monitoring of vendor models
- Reporting vendor risks to audit committees
Module 12. Building an AI-Ready Audit Function
Covers organizational readiness, capability development, and integration into assurance cycles.
12 chapters in this module
- Assessing current audit team capabilities
- Upskilling pathways for auditors
- Integrating AI audits into annual plans
- Collaborating with data science teams
- Developing internal expertise
- Leveraging audit tools for AI
- Creating AI audit checklists
- Standardizing review processes
- Reporting AI risks to leadership
- Benchmarking against peers
- Future-proofing audit approaches
- Leading AI governance initiatives
How this maps to your situation
- You're auditing AI systems without a standardized framework
- You need to validate model fairness but lack clear methods
- Your team is reviewing third-party AI with limited oversight tools
- You're expected to report on AI compliance to leadership
Before vs. after
Before
Uncertainty in how to assess AI systems, reliance on technical teams, reactive documentation, inconsistent audit coverage.
After
Structured, repeatable audit processes for AI, confident validation of compliance, clear documentation, and proactive risk assurance.
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 implementation milestones.
If nothing changes
Continuing without a structured approach risks inconsistent audits, missed compliance gaps, and diminished credibility when AI systems face regulatory scrutiny.
How this compares to the alternatives
Unlike high-level overviews or technical deep dives for data scientists, this course is tailored specifically for audit and compliance professionals in financial services, offering practical, implementation-grade knowledge aligned with regulatory expectations.
Frequently asked
Who is this course designed for?
It's for audit, compliance, and risk professionals in financial services who need to assess AI systems with confidence and precision.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if my institution hasn’t fully adopted AI yet?
Yes. The course prepares audit teams for current and near-future AI deployments, ensuring readiness before systems go live.
$199 one-time. Approximately 45, 60 hours total, designed for self-paced learning with implementation milestones..
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