Description

This curriculum spans the breadth of a multi-workshop program typically delivered during a financial institution’s implementation of BCBS 239 and IFRS 9, covering the data governance practices required to support capital reporting, model accuracy, and regulatory compliance across risk, finance, and technology functions.

Module 1: Defining Capital Access Requirements in Data Governance Strategy

Determine which data assets directly influence credit risk scoring models used by financial institutions.
Map data lineage from source systems to regulatory reporting outputs to identify capital allocation dependencies.
Establish data quality thresholds for loan origination datasets that impact Basel III capital adequacy calculations.
Align data governance objectives with IFRS 9 and CECL accounting standards affecting loan loss provisions.
Identify stakeholders in treasury, risk, and finance who require auditable data for capital planning cycles.
Define metadata requirements for tagging data used in internal capital adequacy assessment processes (ICAAP).
Assess the impact of data latency on real-time capital monitoring dashboards used by executive leadership.
Document data ownership for regulatory capital ratios reported to central banks and prudential regulators.

Module 2: Regulatory Data Standards and Capital Reporting Compliance

Implement BCBS 239 principles for aggregated risk data in capital reporting workflows.
Configure data validation rules to meet COREP and FINREP reporting requirements under CRD/CRR.
Integrate LEI (Legal Entity Identifier) data into counterparty records for accurate exposure measurement.
Design reconciliation processes between internal capital models and regulatory submission datasets.
Enforce data granularity standards required for stress testing under CCAR or EBA exercises.
Classify data subject to SR 11-7 guidance for model risk management in capital estimation.
Implement audit trails for data adjustments made during capital adequacy reconciliation cycles.
Coordinate with compliance teams to update data controls in response to regulatory changes in capital rules.

Module 3: Data Ownership and Accountability for Capital-Critical Systems

Assign formal data stewardship roles for datasets feeding into economic capital models.
Document escalation paths for resolving data discrepancies in capital-at-risk calculations.
Define RACI matrices for data used in internal ratings-based (IRB) capital frameworks.
Implement stewardship workflows for validating exposure data in credit risk systems.
Establish accountability for data used in counter-cyclical capital buffer assessments.
Enforce steward sign-off on data definitions prior to inclusion in capital planning models.
Track stewardship activities through governance tools to demonstrate oversight in audits.
Resolve conflicts between business units over ownership of shared capital-relevant data assets.

Module 4: Data Quality Management for Capital Modeling Accuracy

Define precision and completeness rules for loan-level data used in PD/LGD models.
Implement automated data profiling to detect anomalies in collateral valuation inputs.
Set thresholds for outlier detection in market risk data affecting VaR-based capital charges.
Integrate data quality metrics into model validation documentation for regulatory review.
Monitor timeliness of data feeds from trading systems to capital calculation engines.
Correct systemic data errors in historical loss data used for operational risk capital.
Validate consistency of currency conversion rates applied across global exposure datasets.
Report data quality KPIs to model risk governance committees on a quarterly basis.

Module 5: Metadata Governance for Capital Data Lineage and Transparency

Implement automated lineage capture from transactional systems to capital allocation reports.
Tag data elements used in stress test scenarios with metadata indicating source reliability.
Map data transformations applied to loan portfolio data before inclusion in RWA calculations.
Expose metadata to auditors to demonstrate traceability of capital ratio components.
Integrate business glossary terms with technical metadata for capital adequacy metrics.
Track version history of data models used in internal capital forecasting tools.
Document assumptions embedded in data transformations affecting capital estimates.
Enable self-service access to lineage diagrams for risk analysts validating capital outputs.

Module 6: Data Integration Architecture for Capital Aggregation

Design ETL pipelines to consolidate exposure data from retail, corporate, and trading books.
Implement data virtualization layers to support real-time capital utilization monitoring.
Select integration patterns for synchronizing on-balance and off-balance sheet data.
Enforce referential integrity between counterparty data and exposure records across systems.
Optimize data warehouse schemas to support fast aggregation for capital stress scenarios.
Secure data pipelines carrying confidential capital position data between jurisdictions.
Handle time-zone and calendar discrepancies in global capital data consolidation.
Validate data reconciliation between source systems and centralized risk data lakes.

Module 7: Risk-Adjusted Data Governance for Capital Efficiency

Apply data criticality scoring to prioritize governance efforts on high-impact capital datasets.
Adjust data validation rigor based on the capital sensitivity of downstream models.
Implement tiered data retention policies aligned with capital model back-testing requirements.
Balance data granularity needs against storage costs in capital stress testing environments.
Exempt low-risk data feeds from real-time monitoring to optimize governance resource allocation.
Conduct cost-benefit analysis of data remediation efforts impacting capital ratios.
Use data risk heat maps to guide investment in data controls for capital-critical processes.
Align data incident response protocols with capital reporting blackout periods.

Module 8: Data Governance in M&A and Portfolio Restructuring

Assess data compatibility of acquired portfolios for integration into existing capital models.
Reconcile data definitions for credit risk parameters across merged entities.
Validate data completeness for legacy loans before inclusion in consolidated RWA.
Establish interim data governance protocols during system integration post-acquisition.
Adjust capital allocation models to reflect data quality gaps in newly acquired datasets.
Document data assumptions used in purchase price allocation models requiring capital approval.
Manage data retention and decommissioning of legacy systems in divestiture scenarios.
Report data integration progress to regulators during transitional capital treatment periods.

Module 9: Technology Enablement and Tooling for Capital Data Oversight

Select data governance platforms with built-in support for regulatory taxonomy mapping.
Configure data catalog tools to highlight datasets used in capital adequacy reports.
Integrate data quality monitoring with capital model validation workflows.
Automate data certification processes for quarterly capital submission packages.
Deploy data lineage tools capable of tracing exposures to individual transaction records.
Customize dashboards to show data health metrics for capital planning teams.
Enforce access controls on data used in confidential capital stress test scenarios.
Validate tool interoperability between data governance, risk, and finance systems.