A tailored course, built for your situation
Board-Level AI Compliance for Financial Services for Audit Teams
Master the governance, risk, and audit frameworks shaping AI adoption in regulated financial institutions
$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 that impact financial decisions, yet lack structured frameworks to assess model risk at the board level.
The situation this course is for
AI is now embedded in credit scoring, fraud detection, and trading algorithms across financial institutions. However, audit functions often struggle to evaluate these systems with the rigor expected by regulators and boards. Traditional compliance checklists fail to address dynamic model behavior, data drift, or emergent bias, creating gaps in assurance and strategic oversight.
Who this is for
Compliance officers, internal auditors, risk managers, and technology governance leads in financial services organizations implementing or scaling AI systems.
Who this is not for
This course is not for data scientists building models or executives seeking high-level AI overviews. It is designed specifically for audit and compliance practitioners responsible for validating AI systems within regulated environments.
What you walk away with
- Apply board-ready AI risk assessment frameworks aligned with global financial regulations
- Design audit trails that capture model behavior, data provenance, and decision logic
- Evaluate model fairness, explainability, and robustness using standardized compliance criteria
- Translate technical AI risks into executive-level reports for board consumption
- Implement a repeatable AI compliance audit cycle with built-in adaptation for regulatory updates
The 12 modules (with all 144 chapters)
Module 1. AI Governance in Financial Services
Establish the foundation of AI governance frameworks specific to regulated financial institutions.
12 chapters in this module
- Introduction to AI governance in finance
- Regulatory expectations for AI oversight
- Board responsibilities in AI risk management
- Linking AI governance to enterprise risk frameworks
- Role of audit in governance enforcement
- Case study: Governance failure in a major bank
- Designing governance charters for AI projects
- Stakeholder mapping for AI compliance
- Integrating governance into SDLC
- Metrics for governance effectiveness
- Third-party AI vendor oversight
- Emerging trends in governance standards
Module 2. Regulatory Landscape and Compliance Mapping
Navigate the global regulatory environment and map requirements to AI audit practices.
12 chapters in this module
- Overview of global AI regulations in finance
- Mapping GDPR, CCPA, and AI Act to audit workflows
- SR 11-7 and model risk management updates
- Cross-border compliance challenges
- Regulatory sandboxes and AI
- Compliance gap analysis techniques
- Building a compliance matrix
- Engaging with regulators on AI audits
- Benchmarking against peer institutions
- Regulatory change monitoring systems
- Documentation standards for audits
- Preparing for regulatory exams
Module 3. Model Risk Management for Auditors
Develop audit strategies for assessing AI model risk across development, deployment, and monitoring phases.
12 chapters in this module
- Understanding model risk lifecycle
- Pre-deployment validation requirements
- Ongoing monitoring and revalidation
- Assessing model drift and degradation
- Bias detection in financial models
- Stress testing AI systems
- Scenario analysis for model failure
- Audit evidence for model performance
- Vendor model risk assessment
- Model inventory and registry design
- Model retirement and sunsetting
- Audit tools for model risk
Module 4. AI Audit Frameworks and Methodologies
Implement structured audit methodologies tailored to AI systems in financial contexts.
12 chapters in this module
- Designing AI-specific audit plans
- Risk-based audit scoping for AI
- Control objectives for AI systems
- Testing AI system controls
- Sampling strategies for model outputs
- Audit evidence collection for AI
- Automated audit techniques
- Continuous auditing for AI
- Integrating AI audits into annual plans
- Coordinating with IT and data teams
- Reporting audit findings to management
- Follow-up and remediation tracking
Module 5. Explainability and Transparency in AI
Evaluate and audit AI explainability techniques to ensure transparency and regulatory compliance.
12 chapters in this module
- Principles of explainable AI (XAI)
- Regulatory expectations for model transparency
- Audit techniques for black-box models
- Evaluating SHAP, LIME, and other XAI methods
- Customer-facing explanations in finance
- Documentation of model logic
- Testing explanation consistency
- Bias in explanations
- Explainability in real-time systems
- Trade-offs between accuracy and explainability
- Audit trails for explanation generation
- Board-level communication of model logic
Module 6. Fairness, Bias, and Equity Audits
Conduct audits to detect and mitigate bias in AI systems impacting financial decisions.
12 chapters in this module
- Understanding algorithmic bias in finance
- Legal and regulatory implications of bias
- Audit frameworks for fairness assessment
- Identifying proxy variables
- Disparate impact analysis
- Bias detection tools and metrics
- Testing for intersectional bias
- Mitigation strategies for biased models
- Monitoring fairness over time
- Customer complaint analysis for bias signals
- Reporting bias findings to leadership
- Fairness in credit, lending, and insurance
Module 7. Data Governance and Provenance for AI
Audit data pipelines and governance practices that underpin AI system integrity.
12 chapters in this module
- Data quality requirements for AI
- Audit of data sourcing and collection
- Data lineage and provenance tracking
- Data labeling and annotation audits
- Bias in training data
- Data privacy compliance in AI
- Data access and retention policies
- Third-party data vendor audits
- Data versioning and change control
- Data drift detection
- Audit of synthetic data use
- Data governance maturity assessment
Module 8. AI System Monitoring and Incident Response
Evaluate ongoing monitoring and incident response mechanisms for AI systems.
12 chapters in this module
- Continuous monitoring design for AI
- Key risk indicators for AI systems
- Anomaly detection in model behavior
- Incident classification for AI failures
- Root cause analysis techniques
- Escalation protocols for AI incidents
- Audit of incident response logs
- Post-mortem review processes
- Regulatory reporting of AI incidents
- Recovery and remediation validation
- Monitoring tool validation
- Audit of alert fatigue and response times
Module 9. Third-Party and Vendor AI Audits
Assess and audit AI systems developed or managed by third-party vendors.
12 chapters in this module
- Vendor risk assessment for AI providers
- Audit scope for outsourced AI
- Contractual requirements for AI transparency
- Right-to-audit clauses
- Evaluating vendor model documentation
- On-site vs remote vendor audits
- Assessing vendor change management
- Vendor performance monitoring
- Subcontractor oversight
- Exit strategies and model handover
- Audit of vendor security practices
- Benchmarking vendor compliance maturity
Module 10. Board Communication and Executive Reporting
Develop audit reports and dashboards that effectively communicate AI risk to executive leadership.
12 chapters in this module
- Translating technical findings for executives
- Designing board-level AI risk dashboards
- Key metrics for AI oversight
- Storytelling with audit data
- Presenting risk appetite alignment
- Scenario planning for board discussions
- Communicating uncertainty in AI outcomes
- Visualizing model risk trends
- Preparing Q&A for board sessions
- Linking audit findings to strategic risk
- Executive summary writing
- Follow-up reporting cadence
Module 11. AI Compliance Program Maturity
Assess and improve the maturity of AI compliance programs within financial institutions.
12 chapters in this module
- Maturity models for AI compliance
- Self-assessment techniques
- Gap analysis for compliance programs
- Roadmap development for improvement
- Benchmarking against industry peers
- Resource planning for compliance teams
- Training and capability building
- Technology enablement for compliance
- Change management for new controls
- Measuring program effectiveness
- External validation and certification
- Sustaining compliance culture
Module 12. Future-Proofing AI Audit Practices
Anticipate emerging trends and adapt audit practices for next-generation AI systems.
12 chapters in this module
- Auditing generative AI in finance
- AI in real-time trading systems
- Quantum computing implications
- AutoML and citizen data science risks
- Federated learning audit challenges
- AI in regulatory reporting
- Emerging global standards
- Preparing for AI-specific regulations
- Scenario planning for audit evolution
- Building adaptive audit teams
- Investing in audit automation
- Strategic foresight for compliance leaders
How this maps to your situation
- Auditing AI in credit decisioning systems
- Validating fraud detection models for regulatory exams
- Assessing third-party AI vendors in core banking platforms
- Reporting AI risks to audit committees and boards
Before vs. after
Before
Uncertainty in how to audit complex AI systems, reliance on outdated checklists, and difficulty communicating technical risks to executive leadership.
After
Confidence in applying structured, board-ready audit frameworks to AI systems, with clear documentation, repeatable processes, and executive-aligned reporting.
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 60, 70 hours of focused learning, designed to be completed at your pace over 8, 10 weeks.
If nothing changes
Organizations that lack robust AI audit practices risk regulatory penalties, reputational damage, and loss of board confidence during AI-driven incidents or examinations.
How this compares to the alternatives
Unlike general AI ethics courses or technical model development programs, this course is specifically tailored to audit and compliance professionals in financial services, offering implementation-grade tools, regulatory alignment, and board-level communication strategies not found in academic or vendor-led training.
Frequently asked
Who is this course designed for?
Compliance officers, internal auditors, risk managers, and technology governance professionals in financial institutions who need to audit AI systems with board-level accountability.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a digital certificate of completion is issued through the Art of Service learning environment after finishing all modules.
$199 one-time. Approximately 60, 70 hours of focused learning, designed to be completed at your pace over 8, 10 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