A tailored course, built for your situation
Governance for Data-Driven Financial Institutions
A compliance and risk framework for analytics teams in banking and credit unions
$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.
Your analytics initiatives are only as strong as the governance behind them , and regulators are watching.
The situation this course is for
Data teams in financial services are under pressure to deliver insights faster, but missteps in model transparency, data lineage, or compliance controls can trigger audits, fines, or reputational damage. Traditional governance moves too slowly, while unchecked analytics introduces risk. There’s no playbook for balancing innovation with accountability , until now.
Who this is for
Senior consultants and analytics leads in financial services who must align data projects with compliance, audit, and regulatory expectations.
Who this is not for
Entry-level analysts, pure IT staff, or executives seeking high-level overviews without implementation detail.
What you walk away with
- Align analytics projects with FFIEC, GLBA, and SR 11-7 expectations
- Implement model risk management for predictive analytics and AI
- Document data lineage and decision logic for audit readiness
- Integrate governance into agile analytics workflows without slowing innovation
- Communicate compliance posture confidently to auditors and executives
The 12 modules (with all 144 chapters)
Module 1. The Regulatory Landscape for Financial Analytics
Understand which rules apply to data projects in banking and credit unions, including FFIEC guidance, GLBA, and SR 11-7. Learn how regulators assess model risk and data governance. Identify enforcement trends and common deficiencies in audit findings. Build a compliance baseline for all analytics initiatives.
12 chapters in this module
- Overview of financial regulations
- SR 11-7 and model risk
- GLBA data protection rules
- FFIEC analytics guidance
- Regulatory expectations by asset size
- Enforcement actions and lessons
- Compliance by design principle
- Mapping rules to use cases
- Risk-based tiering of models
- Documentation standards
- Audit preparation checklist
- Regulator communication tactics
Module 2. Governance Frameworks for Analytics Teams
Adapt COBIT, COSO, and NIST frameworks to data analytics workflows. Focus on control objectives specific to data pipelines, model development, and reporting. Learn how to operationalize governance without slowing delivery. Implement tiered oversight based on risk level and business impact.
12 chapters in this module
- COBIT for data projects
- COSO integration points
- NIST Cybersecurity Framework
- Control objectives mapping
- Risk-based governance tiers
- Oversight committee roles
- Escalation pathways
- Change control process
- Version control for models
- Access control policies
- Data stewardship roles
- Governance KPIs
Module 3. Model Risk Management Fundamentals
Apply SR 11-7 principles to predictive models in credit scoring, churn prediction, and customer segmentation. Learn validation requirements, documentation standards, and lifecycle controls. Build defensible processes that satisfy internal audit and regulators.
12 chapters in this module
- Model lifecycle stages
- Pre-deployment validation
- Independent review process
- Model inventory standards
- Performance monitoring
- Thresholds and triggers
- Retirement criteria
- Documentation templates
- Third-party model risks
- Validation automation
- Model risk self-assessment
- Audit response protocol
Module 4. Data Lineage and Provenance Tracking
Ensure traceability from raw data to final insight. Implement lineage practices that support auditability and debugging. Use metadata standards and tools to automate tracking across pipelines. Demonstrate data integrity to compliance teams.
12 chapters in this module
- Data lineage principles
- Metadata capture methods
- Pipeline documentation
- Automated lineage tools
- Source-to-report mapping
- Change impact analysis
- Versioned data sets
- Data quality flags
- Provenance reporting
- Audit trail generation
- Lineage for AI models
- Cross-system tracing
Module 5. Bias Detection and Fair Lending Compliance
Identify and mitigate bias in analytics used for lending, marketing, and risk scoring. Apply fair lending principles to model design and output. Document testing procedures to demonstrate compliance with ECOA and CRA.
12 chapters in this module
- ECOA compliance basics
- CRA considerations
- Bias in training data
- Disparate impact testing
- Adverse action logic
- Fair lending benchmarks
- Model fairness metrics
- Segment analysis methods
- Remediation workflows
- Documentation for examiners
- Third-party vendor oversight
- Ongoing monitoring plan
Module 6. Privacy and Data Protection in Analytics
Apply GLBA, CCPA, and other privacy rules to analytics workflows. Implement data minimization, access controls, and de-identification techniques. Balance insight generation with consumer privacy rights.
12 chapters in this module
- GLBA Safeguards Rule
- CCPA compliance scope
- PII handling standards
- Data minimization tactics
- Access control models
- De-identification methods
- Re-identification risks
- Data retention policies
- Breach response planning
- Vendor data agreements
- Consumer data rights
- Privacy by design
Module 7. Audit Readiness for Analytics Teams
Prepare for internal and external audits with structured documentation, evidence trails, and communication protocols. Learn what auditors look for in model risk, data governance, and compliance controls.
12 chapters in this module
- Audit preparation cycle
- Evidence collection plan
- Document organization
- Response drafting
- Interview preparation
- Deficiency tracking
- Corrective action plans
- Management response letters
- Follow-up timelines
- Regulatory inquiry handling
- Audit communication rules
- Post-audit review
Module 8. Stakeholder Communication for Compliance
Translate technical analytics work into clear, compliant narratives for executives, auditors, and regulators. Build confidence through transparency and structured reporting.
12 chapters in this module
- Executive summary writing
- Risk communication tactics
- Regulator briefing prep
- Board reporting formats
- Audit liaison role
- Compliance storytelling
- Issue escalation scripts
- Status reporting templates
- Cross-functional alignment
- Crisis communication plan
- Regulatory inquiry response
- Presentation best practices
Module 9. Third-Party and Vendor Risk in Analytics
Assess and manage risks from external data sources, cloud platforms, and AI vendors. Implement due diligence, contract controls, and ongoing monitoring.
12 chapters in this module
- Vendor due diligence
- Cloud service risks
- Data licensing terms
- API security review
- Subprocessor oversight
- Contractual controls
- Performance monitoring
- Exit planning
- Compliance certification
- Audit rights negotiation
- Incident response clauses
- Vendor risk scoring
Module 10. Change Management for Governance Adoption
Drive adoption of governance practices across teams resistant to compliance overhead. Use change frameworks to align incentives, reduce friction, and build ownership.
12 chapters in this module
- Resistance identification
- Stakeholder analysis
- Change champions
- Training rollout plan
- Incentive alignment
- Feedback loops
- Pilot program design
- Scaling strategy
- Communication calendar
- KPIs for adoption
- Governance maturity model
- Continuous improvement
Module 11. Incident Response for Data and Model Failures
Prepare for model drift, data corruption, or compliance breaches with structured response plans. Minimize damage and demonstrate control during crises.
12 chapters in this module
- Incident classification
- Response team roles
- Containment procedures
- Root cause analysis
- Regulatory notification
- Public statement prep
- Remediation tracking
- Post-mortem process
- Model rollback plan
- Data recovery steps
- Legal counsel coordination
- Lessons learned report
Module 12. Sustaining Governance Over Time
Maintain governance effectiveness as teams, tools, and regulations evolve. Implement review cycles, updates, and continuous improvement to avoid decay.
12 chapters in this module
- Annual review process
- Regulation monitoring
- Policy update workflow
- Training refresh cycle
- Tooling upgrades
- Benchmarking peers
- Maturity assessments
- Audit feedback loop
- Resource planning
- Succession planning
- Budget justification
- Future-proofing strategy
How this maps to your situation
- Regulatory scrutiny in financial services
- Model risk management in analytics
- Audit readiness for data teams
- Governance adoption in agile environments
Before vs. after
Before
Analytics projects move fast but lack compliance rigor, creating audit risk and leadership mistrust.
After
Governance is embedded by design, enabling faster, defensible innovation with clear accountability.
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 3 hours per module, designed for integration with active projects.
If nothing changes
Without structured governance, even high-performing analytics teams face regulatory penalties, audit failures, and project rollbacks , eroding trust and budget.
How this compares to the alternatives
Generic compliance courses lack financial services context. Internal playbooks are incomplete. This course delivers a complete, field-tested framework tailored to data-driven banks and credit unions.
Frequently asked
Who is this course for?
Senior consultants, analytics leads, and compliance officers in financial institutions implementing data governance.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant for credit unions?
Yes, the frameworks apply to all financial institutions, with examples tailored to credit union scale and structure.
$199 one-time. Approximately 3 hours per module, designed for integration with active projects..
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