BIRD and GenAI: Building a Comprehensive Reporting Dictionary

How I use GenAI to transform the ECB’s BIRD initiative into metadata, ERDs, and dbt scaffolding

Summary

The ECB’s BIRD initiative defines a comprehensive reporting dictionary for banks, covering parties, groups, securities, positions, and ratings. I’ve applied GenAI to extract BIRD’s metadata, generate ERDs, and scaffold dbt models. BIRD demonstrates how a regulatory model can evolve into a universal reference LDM, complementing AnaCredit and RRE.

Key Takeaways

• BIRD scope: Party/Group structures, Instruments, Securities, Positions, Ratings.
• Challenge: breadth and normalization — covering multiple reporting needs in one model.
• GenAI role: parsing definitions, aligning with other models, generating YAML + dbt scaffolding.
• Outcome: reusable subject-area models for Parties, Groups, Securities, Positions.
• Lesson: BIRD shows how regulations can seed universal data models if treated as metadata.

The Context: What is BIRD?

The Banks’ Integrated Reporting Dictionary (BIRD) is an ECB initiative to standardize reporting requirements across Europe. It defines:

• Party / Group: organizations and their memberships.
• Instruments / Securities: financial products and identifiers (ISIN, types).
• Positions: holdings and balances at reporting dates.
• Ratings: credit quality assessments across systems.

This breadth makes BIRD more than a regulation — it’s a reporting meta-model. I see it as closely aligned to what I described in How Universal Data Models Support Business-Oriented Data Modeling: stable anchors that can serve multiple use cases.

The Challenge: Too Broad, Too Normalized

BIRD’s scope is impressive, but it’s also highly normalized. Without tooling, it feels overwhelming.

This reminds me of the gap I highlighted in From Coding to Modeling: The Rise of Model-Driven Data Engineering: complexity must be captured as metadata first, then re-expressed as executable, business-friendly models.

How I Apply GenAI to BIRD

Here’s how I approached it:

1. Parse PDFs + GitHub repo → extract entities like Party, Group, Position, Rating System.
2. Normalize names + definitions → align with AnaCredit’s Counterparty and RRE’s Property.
3. Emit YAML → machine-readable specs for dbt scaffolding.
4. Generate ERDs → clear subject-area diagrams.
5. Build dbt models → sources, staging, marts for Parties, Groups, Securities, Positions.

This process directly reflects the metadata-first philosophy I also wrote about in Metadata as Data — Controlling and Governing Data Models.

Example YAML (BIRD Party/Group)

entities:
  party:
    keys: [party_id]
    attributes:
      - lei
      - name
      - country_code
  group:
    keys: [group_id]
    attributes:
      - group_name
  party_group_membership:
    keys: [party_id, group_id]
    attributes:
      - valid_from
      - valid_to

ERD

Press enter or click to view image in full size

(Mermaid — convert via mermaid.live)

This ERD highlights roles and associations, a pattern I emphasized in Cooking with Data: How Business-Friendly Mapping Brings Clarity to ETL.

dbt Scaffold (Party Staging)

-- stg_bird__party.sql
select
  party_identifier as party_id,
  lei,
  party_name as name,
  country_code
from {{ source('bird','party') }};

This produces a clean, reusable view that aligns with AnaCredit’s ldm__counterparty.

Lessons from BIRD

• Breadth: covers most of the reporting landscape in one model.
• Normalization: great for governance, but needs abstraction for usability.
• Reusability: Party, Group, Security, Position entities are enterprise anchors.
• Mapping potential: aligns naturally with AnaCredit’s Counterparty and RRE’s Instrument/Property links.

Why It Matters

BIRD demonstrates that regulatory models can be more than compliance requirements — they can seed universal data models.

This resonates with what I shared in Bill Inmon’s Lasting Impact — normalized structures and universal anchors remain powerful, especially when combined with GenAI and dbt for execution.

What’s Next

Next, I’ll bring the strands together: AnaCredit, RRE, and BIRD. I’ll show how I use GenAI to propose cross-model mappings and construct a generic LDM that works as a financial services integration backbone.