2        Introduction to Oracle Financial Services Data Governance for US Regulatory Reporting

This chapter provides a brief overview of the BCBS 239 Principles and Oracle Financial Services Data Governance for US Regulatory Reporting (DGRR).

Overview of the Basel Committee on Banking Supervision (BCBS) 239

The Basel Committee on Banking Supervision (BCBS) 239: Principles for Effective Risk Data Aggregation and Risk Reporting are also known as the 14 principles. These principles were developed because, many banks lack "the ability to aggregate risk exposures and identify concentrations quickly and accurately at the bank group level, across business lines, and between legal entities." The BCBS 239 framework is intended to strengthen the risk data aggregation and reporting practices of the banks. BCBS 239 is designed to drive more timely information and better strategic planning and reduce the impact of losses.

Oracle Financial Services Analytical Applications Solution for BCBS 239 Regulations

The Oracle Financial Services Analytics Applications (OFSAA) unified platform creates a foundation to address the regulatory requirements and successful BCBS 239 compliance, by providing a common data infrastructure that:

·        Builds a single source of truth

·        Enables effective data usage

·        Supports comprehensive and consolidated reporting

The following table describes the 14 principles of BCBS 239:

Table 1: BCBS 239 Principles Answered by Respective OFSAA Components

 

 

BCBS 239 Principles Answered by Respective OFSAA Components

BCBS 239 Principle

Description

OFSA Application Catering to the Principle

BCBS Principle Category: Overarching Governance and Infrastructure

Principle 1: Governance

Identification, assessment, and management of data quality risks to be a part of a bank’s risk management framework.

Risk data aggregation and risk reporting practices must be fully documented and validated, extended to new initiatives, unaffected by the organization structure.

Awareness of the limitations of full risk data aggregation.

This principle is addressed by the Data Quality Framework and OFS Model Risk Management.

Principle 2: Data Architecture and IT Infrastructure

Integrated data taxonomies and architecture across the group.

Establish roles and responsibilities to ensure adequate controls.

This principle is addressed by OFS Data Foundation, OFS Analytical Applications Infrastructure, and OFS Enterprise Modeling Framework.

BCBS Principle Category: Risk Data Aggregation Capabilities

Principle 3: Accuracy and Integrity

Ensure that the risk data aggregation is accurate and reliable with adequate controls, data reconciliation, and a single source of data for each risk type.

Documentation of risk data aggregation process.

Establish escalation channels and action plans.

This principle is addressed by OFS Analytical Applications Infrastructure, OFS Enterprise Modeling Framework, OFS Reconciliation Framework, OFS Data Foundation, and OFS Operational Risk.

Principle 4: Completeness

Capture all material risk data by the relevant dimensions.

Any exceptions to completeness must be identified and documented.

Consistent risk data aggregation capabilities.

This principle is addressed by OFS Data Foundation, OFS Analytical Applications Infrastructure, OFS Enterprise Modeling Framework, and all OFS Applications.

Principle 5: Timeliness

Generate aggregated data as per the desired frequency.

This principle is addressed by OFS Analytical Applications Infrastructure, OFS Applications, and Exadata Benchmarks.

BCBS 239 Principle

Description

OFSA Application Catering to the Principle

Principle 6: Adaptability

Flexibility to meet ad-hoc requests, especially during stress.

Incorporate changes related to internal and external business factors and regulatory frameworks.

Generate sub-sets of data based on specific dimensions.

This principle is addressed by all OFS Applications with OFS Data Foundation, OFS Analytical Applications Infrastructure, OFS Enterprise Modeling Framework.

BCBS Principle Category: Risk Reporting Practices

Principle 7: Accuracy

Reports must be reconciled with risk data, validations to be applied to the output, and exception reports to be displayed.

Establish the reliability of approximations such as output from models, scenarios, and stress tests.

This principle is addressed by all the standalone OFS BI analytics applications and dashboards of all the OFS applications.

Principle 8: Comprehensiveness

Cover all material risks including credit, market, operational and liquidity risks, capital adequacy, stress testing.

Exposure and position data, concentrations, limits, risk appetite.

This principle is addressed by all the standalone OFS BI analytics applications and dashboards of all the OFS applications.

Principle 9: Clarity and Usefulness

Reports must contain risk data, analysis, interpretation, and qualitative information.

Customized to suit individual requirements.

This principle is addressed by all the standalone OFS BI analytics applications, and dashboards of all the OFS applications, and Oracle Business Intelligence Enterprise Edition (OBIEE).

Principle 10: Frequency

Produce reports at the desired frequency.

Timely availability of reports under stress conditions.

This principle is addressed by all the OFS BI analytics application, OFS applications which compute metrics, and OFS Enterprise Modeling Framework.

Principle 11: Distribution

Make reports available to relevant stakeholders on time while maintaining confidentiality.

This principle is addressed by all the standalone OFS BI analytics applications, and dashboards of all the OFS applications, and Oracle Business Intelligence Enterprise Edition (OBIEE).

BCBS Principle Category: Supervisory Review, Tools and Cooperation

BCBS 239 Principle

Description

OFSA Application Catering to the Principle

Principle 12: Review

Supervisors must:

Review bank compliance with principles 1 to 11.

Examine the results of internal and external audits.

Test bank’s data aggregation and reporting capabilities under normal and stress conditions.

This principle is addressed by OFS Data Governance for US Regulatory Reporting.

Principle 13: Remedial Actions and Supervisory Measures

Use of multiple tools for:

Required remedial action

Increased scrutiny

Independent review

Capital add-ons

This principle is addressed by OFS Data Governance for US Regulatory Reporting.

Principle 14: Home/Host cooperation

Supervisors of relevant jurisdictions must cooperate.

Information and experience-sharing through bilateral or multilateral dialogue.

 

 

Overview of Data Governance for US Regulatory Reporting

Data Governance for US Regulatory Reporting (DGUSRR) operationalizes the data governance process. The OFSAA DGUSRR enables financial institutions to map multiple data sources to a standard, common business glossary.

Additionally, DGUSRR enables you to:         

·        Identify all critical data elements.

·        Track and monitor the data elements from their source to the eventual usage in reporting.

·        Manage Regulatory submissions.

·        Establish a governance process around the data elements and reporting process to offer greater visibility and increased confidence in the organization for the board of directors and regulators.

·        Consolidate and collaborate across the enterprise providing a truly unified enterprise data management process.

·        The following diagram provides a high-level workflow of the DG application.

Figure 1: Technical Architecture

·         

Description of Data Governance for US Regulatory Reporting page follows This illustration displays Data Governance for US Regulatory Reporting (DGUSRR) operationalizes the data governance process. The OFSAA DGUSRR enables financial institutions to map multiple data sources to a standard, common business glossary.

The content provided to DGUSRR helps the customer to have access to over 20,000 business terms and definitions that form a part of the Metadata Glossary.

Important Features of Data Governance for US Regulatory Reporting

The following are the key features:

·        It provides a business glossary for standardization.

·        Defines operational and quality controls on every data element and monitors the effectiveness of controls.

·        Monitors all key metrics, trends, and variances on data elements.

·        Defines maintain and track regulatory report submissions.

·        Completes data quality dashboards.