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FIN1064 Mastering Basel III for Big Data Application Software Managers

$199.00
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A tailored course, built for your situation

Mastering Basel III for Big Data Application Software Managers

Build defensible, source-backed implementation logic that holds up under peer review

$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.

Who this is for

Senior technical leader in financial services data infrastructure, responsible for systems that feed risk and compliance reporting under regulatory frameworks like Basel III.

Who this is not for

Junior developers, auditors looking for control checklists, or compliance analysts seeking policy summaries will not benefit from this course.

What you walk away with

  • Articulate the technical rationale behind data architecture choices in Basel III contexts using primary sources
  • Reference specific paragraphs in the Basel III framework to justify thresholds, categorisations, and aggregation logic
  • Respond confidently to peer challenges on data lineage, model input validity, and reporting consistency
  • Use EBA Q&A documents and national regulator interpretations to ground implementation decisions
  • Document a defensible audit trail from raw data to final capital calculation

The 12 modules (with all 144 chapters)

Module 1. Basel III and the Role of Big Data Infrastructure
Understand how large-scale data systems directly feed into capital adequacy calculations and where software design intersects with regulatory obligation.
12 chapters in this module
  1. How Basel III capital ratios depend on underlying data pipelines
  2. Distinguishing operational vs. regulatory data requirements
  3. Mapping data freshness to reporting cycle deadlines
  4. Understanding materiality thresholds in aggregated exposures
  5. The impact of data latency on liquidity coverage ratios
  6. When big data becomes a compliance liability
  7. Integrating governance without slowing innovation
  8. Balancing scalability with auditability in data models
  9. Documenting data provenance for reviewer access
  10. Linking schema changes to compliance impact assessments
  11. Common pitfalls in credit risk data aggregation
  12. Case example: Data drift and its regulatory consequences
Module 2. The Structure of the Basel Framework
Navigate Basel I, II, and III as a technical practitioner, focusing on provisions that touch data systems.
12 chapters in this module
  1. Core principles behind Basel III’s enhanced capital standards
  2. How Pillar 1 drives technical implementation choices
  3. Pillar 2’s implications for internal model validation
  4. Pillar 3 disclosure requirements and data sourcing
  5. Linking leverage ratio calculations to data inputs
  6. Understanding net stable funding ratio dependencies
  7. Differences between US and EU Basel implementations
  8. Role of national regulators in interpreting Basel rules
  9. Using FRB and OCC guidance to inform software logic
  10. Mapping internal control frameworks to Basel mandates
  11. Key Basel revisions from the current cycle to current cycle
  12. Timeline of major Basel implementation phases
Module 3. Defining Regulatory Data Scope
Identify which data elements fall under Basel III monitoring and which do not.
12 chapters in this module
  1. List of data types captured in leverage ratio reporting
  2. Exposures subject to counterparty credit risk rules
  3. Derivatives valuation data and collateral tracking
  4. What qualifies as off-balance-sheet exposure
  5. Treatment of intercompany loans in reporting
  6. Data tagging requirements for intraday liquidity
  7. Thresholds for reporting large exposures
  8. Treatment of secured vs. unsecured lending data
  9. Inclusion rules for contingent liabilities
  10. Currency conversion rules for cross-border portfolios
  11. Treatment of non-performing loans in data sets
  12. When subcontracted processing triggers reporting duty
Module 4. Data Quality and Regulatory Acceptance
Ensure your data meets the bar for supervisory review.
12 chapters in this module
  1. Completeness standards in capital reporting submissions
  2. Accuracy expectations for exposure-at-default metrics
  3. Timeliness requirements across reporting cycles
  4. Consistency of data across time and systems
  5. Verifiability of data transformations in pipelines
  6. Auditability of model input selection
  7. Handling missing data in regulatory reports
  8. Documentation standards for data lineage
  9. Data reconciliation with core banking systems
  10. Version control for regulatory data sets
  11. Handling data corrections post-submission
  12. Common data quality failures in exams
Module 5. Model Input Governance
Establish controls for inputs that feed Basel-compliant outputs.
12 chapters in this module
  1. Identifying model-critical data fields in pipelines
  2. Input validation rules based on Basel guidance
  3. Data cleansing thresholds and disclosure
  4. Handling outliers in risk-weighted asset calculations
  5. Capping logic for high-leverage exposures
  6. Treatment of zero-value but high-risk positions
  7. Data normalisation across business lines
  8. Source system hierarchy for conflict resolution
  9. Audit trail requirements for input changes
  10. Change management for model parameters
  11. Versioning input data sets for reproducibility
  12. Documenting assumptions in data preprocessing
Module 6. Aggregation Logic and Consistency
Implement standardized aggregation that aligns with Basel expectations.
12 chapters in this module
  1. Consolidation rules for cross-entity exposures
  2. Currency translation methodology under Basel
  3. Time aggregation for liquidity coverage reporting
  4. Rounding rules that preserve material accuracy
  5. Handling intra-day vs. end-of-day balances
  6. Netting rules for derivatives positions
  7. Offsetting collateral across counterparties
  8. Treatment of guarantees and credit enhancements
  9. Group-wide vs. legal-entity aggregation
  10. Thresholds for material aggregation differences
  11. Reconciliation with general ledger data
  12. Reporting large exposures at highest consolidation level
Module 7. Liquidity Coverage Ratio Implementation
Operationalise LCR data requirements in software systems.
12 chapters in this module
  1. Identifying cash inflows subject to stress assumptions
  2. Categorising high-quality liquid assets
  3. Stock vs. flow data in liquidity reporting
  4. Time bucketing for cash flow projections
  5. Dependence on wholesale funding sources
  6. Data requirements for stress scenario inputs
  7. Treatment of central bank access in reserves
  8. Monitoring intraday liquidity gaps
  9. Data thresholds for triggering escalation
  10. Reporting frequency and data retention
  11. Common misclassifications in LCR submissions
  12. Case study: Liquidity data failure at peer institution
Module 8. Net Stable Funding Ratio Setup
Capture NSFR data elements and implement funding classifications.
12 chapters in this module
  1. Defining required stable funding by instrument type
  2. Available stable funding data inputs
  3. Maturity ladder construction for liabilities
  4. Weighting retail deposits by stability
  5. Treatment of interbank funding data
  6. Data segmentation for operational deposits
  7. Time horizon for funding data aggregation
  8. Treatment of secured vs. unsecured funding
  9. Reporting granularity for NSFR disclosures
  10. Reconciliation with balance sheet reporting
  11. Thresholds for material NSFR variances
  12. Documentation of funding assumptions
Module 9. Large Exposure Reporting
Track and report concentration risk in data systems.
12 chapters in this module
  1. Defining connected counterparties in data model
  2. Aggregation rules for related entities
  3. Data fields required for exposure testing
  4. Threshold calculations based on tier 1 capital
  5. Treatment of guarantees and cross-guarantees
  6. Intra-day exposure monitoring data
  7. Reporting frequency for large exposure breaches
  8. Documentation of mitigation data
  9. Treatment of secured lending in exposures
  10. Data lineage for cross-entity loans
  11. Regulatory thresholds by jurisdiction
  12. Case example: Overlooked exposure at global bank
Module 10. Pillar 2 and Internal Capital Adequacy
Support ICAAP processes with robust, auditable data.
12 chapters in this module
  1. Data needs for stress testing scenarios
  2. Internal risk categorisation methodology
  3. Granularity required for risk migration analysis
  4. Documentation of risk parameter assumptions
  5. Backtesting data requirements
  6. Model validation data inputs
  7. Reporting internal capital breaches
  8. Data retention for supervisory review
  9. Linking ECL models to capital planning
  10. Treatment of emerging risk in data sets
  11. Scenario design and data coverage
  12. Audit trail for capital model adjustments
Module 11. Regulatory Inquiry Readiness
Prepare to respond to examiner questions with precision.
12 chapters in this module
  1. Common Basel-related questions from examiners
  2. Documentation structure for data decisions
  3. Preparing lineage maps for reviewer access
  4. Responding to threshold justification queries
  5. Explaining data treatment adjustments
  6. Citing Basel text to support implementation choices
  7. Using supervisory guidance to defend design
  8. Preparing cross-system reconciliation reports
  9. Handling requests for sample data sets
  10. Timeline for responding to data inquiries
  11. Engagement protocols with compliance teams
  12. Lessons from recent enforcement actions
Module 12. Sustaining Compliance Through Change
Maintain defensibility amid system upgrades and business shifts.
12 chapters in this module
  1. Change impact assessment for Basel-relevant systems
  2. Versioning control for reporting code
  3. Testing protocols for data pipeline updates
  4. Regression testing for capital models
  5. Documentation updates for schema changes
  6. Peer review process for model modifications
  7. Handling acquisitions and data integration
  8. Decommissioning data sets with compliance impact
  9. Monitoring third-party data provider changes
  10. Audit readiness during transition periods
  11. Succession planning for key data roles
  12. Updating implementation playbook annually

How this maps to your situation

  • Current scrutiny on data-driven capital models
  • Need for defensible technical design choices
  • Rising peer challenge on implementation logic
  • Expectation of source-backed reasoning in reviews

Before vs. after

Before
Responding to peer challenges with general rationale
After
Walking through the why with specific sources, examples, and implementation logic

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 8 hours total, designed for completion across a single weekend or weekday evenings.

If nothing changes
Without deep familiarity with Basel III’s technical requirements, data architecture decisions may lack defensibility, leading to rework, scrutiny, or misalignment with risk and compliance teams.

How this compares to the alternatives

Unlike generic compliance summaries or certification prep courses, this program focuses exclusively on the technical implementation of Basel III in big data environments , giving you the precise reasoning and source references needed to defend design choices under peer review.

Frequently asked

Is this course relevant if I’m not in a compliance role?
Yes. It’s designed for technical leaders like Big Data Application Software Managers who build and oversee systems that feed into regulatory reporting.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does it cover US-specific implementation rules?
Yes. The course includes FRB, OCC, and Federal Reserve interpretations relevant to PNC’s context.
$199 one-time. Approximately 8 hours total, designed for completion across a single weekend or weekday evenings..

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
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