RRDF – Regulatory Reporting Data Foundation¶
A specialization of the Cygnet Banking Data Foundation (CBDF)
Creating a canonical data model between banking systems and regulatory reporting requirements.
1. Introduction¶
The Regulatory Reporting Data Foundation (RRDF) is a regulatory-reporting-focused implementation of the broader Cygnet Banking Data Foundation (CBDF).
RRDF provides a canonical data model that bridges the gap between source banking systems and regulatory reporting requirements.
Its purpose is to create a stable, reusable representation of banking information that remains independent of:
- Core banking vendors
- Source system structures
- Regulatory form designs
- Reporting technologies
- Validation technologies
RRDF serves as the data foundation for the Cygnet Regulatory Intelligence Platform.
2. Why RRDF Exists¶
Every regulatory reporting implementation encounters the same challenge.
A regulator requests:
"Report the outstanding financing amount."
The bank asks:
"Which table?"
The real answer is:
"It depends."
The same business concept may be stored differently across institutions:
- Different table structures
- Different naming conventions
- Different product hierarchies
- Different customer models
- Different accounting representations
As a result, every implementation begins by solving the same mapping problem.
RRDF was created to eliminate repeated reinvention.
3. Historical Background¶
Over the last two decades, Cygnet has worked with:
- Multiple core banking platforms
- Multiple data warehouse architectures
- Multiple regulatory reporting engines
- Multiple regulators
Despite differences in technologies and institutions, the underlying business concepts remained remarkably similar.
Examples include:
- Customer
- Account
- Facility
- Loan
- Deposit
- Collateral
- Branch
- Transaction
- Exposure
This observation led to the development of a canonical banking data foundation.
The broader initiative became known as:
CBDF – Cygnet Banking Data Foundation
RRDF represents the regulatory reporting domain built on top of CBDF principles.
4. Vision¶
Our vision is:
Define banking data once and reuse it across reporting, compliance, analytics, and governance initiatives.
Instead of creating separate data models for every project, RRDF provides a reusable foundation.
5. Core Principles¶
Business Before Technology¶
Data models should reflect business concepts rather than physical database structures.
Canonical Representation¶
Each business concept should have a single canonical representation.
Regulatory Independence¶
The model should remain stable even when regulatory forms change.
Source System Independence¶
The model should remain stable even when source systems change.
Reusability¶
The same data structures should support:
- Regulatory reporting
- Data quality
- Analytics
- Governance
- Data lineage
6. Relationship with CBDF¶
CBDF is the enterprise-level banking data foundation.
It encompasses multiple business domains.
Examples include:
- Customer Domain
- Product Domain
- Finance Domain
- Risk Domain
- Treasury Domain
- Regulatory Domain
RRDF focuses specifically on the regulatory reporting domain.
CBDF
│
├── Customer Domain
├── Product Domain
├── Finance Domain
├── Risk Domain
├── Treasury Domain
└── Regulatory Domain (RRDF)
7. Position Within the Zensical Architecture¶
CRRR
Regulatory Knowledge
|
v
RRDF
Canonical Regulatory Data Model
|
v
CDAP
Compliance Assets
|
v
CRRS
Execution Platform
RRDF is the bridge between regulatory knowledge and operational execution.
8. Business Entity Model¶
The foundation of RRDF is a business entity model.
The objective is to describe banking information in terms of business entities rather than source tables.
Party Domain¶
Represents participants in banking relationships.
Examples:
- Individual Customers
- Corporate Customers
- Guarantors
- Related Parties
Typical attributes include:
- Party Identifier
- Name
- Legal Status
- Economic Sector
- Residency
Product Domain¶
Represents banking products.
Examples:
- Loans
- Financing
- Deposits
- Savings
- Current Accounts
- Trade Finance
Facility Domain¶
Represents contractual facilities.
Examples:
- Credit Facilities
- Financing Facilities
- Credit Lines
- Limits
Account Domain¶
Represents operational accounts associated with facilities and products.
Collateral Domain¶
Represents assets securing obligations.
Examples:
- Property
- Vehicles
- Cash Collateral
- Guarantees
Financial Domain¶
Represents balances and financial positions.
Examples:
- Outstanding Balance
- Principal
- Interest
- Accrued Amounts
- Provisions
Regulatory Domain¶
Represents reporting-specific structures.
Examples:
- Reporting Classifications
- Reporting Dimensions
- Regulatory Attributes
9. Logical Architecture¶
+-----------------------+
| Party Domain |
+-----------+-----------+
|
v
+-----------------------+
| Product Domain |
+-----------+-----------+
|
v
+-----------------------+
| Facility Domain |
+-----------+-----------+
|
v
+-----------------------+
| Financial Domain |
+-----------+-----------+
|
v
+-----------------------+
| Regulatory Domain |
+-----------------------+
10. Regulatory Mapping Layer¶
One of the most important responsibilities of RRDF is maintaining mappings between business entities and regulatory requirements.
Examples:
This allows regulatory requirements to evolve without requiring redesign of the underlying data model.
11. Current Focus Areas¶
The current implementation is focused on:
Antasena¶
Primary pilot initiative.
SLIK¶
Secondary implementation target.
Common Regulatory Concepts¶
Identification of reusable entities shared across reporting frameworks.
Metadata-Driven Mapping¶
Reducing hard-coded transformations through metadata.
12. Long-Term Direction¶
Future enhancements may include:
Semantic Layer¶
Business-friendly representation of banking concepts.
Data Lineage¶
Traceability from source systems to reporting outputs.
Industry Reference Model¶
Expansion toward a broader banking industry reference model.
Cross-Regulatory Reuse¶
Support multiple regulators using the same canonical structures.
AI-Assisted Mapping¶
Automated identification of source-to-target mappings.
13. Success Criteria¶
RRDF is successful when:
- New regulatory forms require minimal data model changes.
- New source systems can be integrated consistently.
- Validation rules can be reused across projects.
- Reporting implementations become faster.
- Business concepts remain stable despite technology changes.
14. Strategic Importance¶
Historically, most regulatory reporting projects focused on report generation.
RRDF recognizes that the true challenge lies earlier in the lifecycle.
The majority of project effort is spent:
- Understanding source systems
- Mapping business concepts
- Reconciling inconsistencies
- Standardizing data
RRDF addresses this challenge directly by creating a reusable regulatory reporting data foundation.
15. Project Motto¶
Model Once. Reuse Everywhere.
RRDF provides the canonical regulatory reporting data model that connects regulatory knowledge, compliance assets, and execution platforms into a unified architecture.