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12,000 Hours Saved With AI Data Lineage
Sia helped a leading U.S. bank build an AI pipeline that reconstructs end-to-end data lineage for its FR 2052a liquidity reporting, saving an estimated 12,000 hours and cutting root-cause analysis from weeks to minutes.
Context
The FR 2052a liquidity report draws on a highly complex data landscape, and the Federal Reserve uses it to monitor large banks' liquidity profiles. When a data defect appears, experts need to trace where each figure comes from, which can take weeks or months in deeply nested ETL processes.
A leading U.S. bank wanted transparent data lineage for its FR 2052a liquidity reporting.
Client Challenge
The bank faced:
- A highly complex, deeply nested reporting ETL landscape with limited visibility into transformations for subject matter experts.
- The need for an AI-driven solution to extract and analyze ETL objects, stored procedures, functions and views, and to reconstruct end-to-end lineage.
What Sia Did
Sia built an AI pipeline on the client's in-house infrastructure:
- Step-by-step analysis: a well-engineered AI pipeline analyzing ETL logic step by step and highlighting transformations at each step.
- Explainable outputs: lineage outputs tailored for FR 2052a use cases and for subject matter experts, with security, scalability and regulatory alignment.
Results & Outlook
The solution saved an estimated 12,000 hours:
- Subject matter experts actively used AI-generated lineage across attributes, products and tables for defect investigations and impact analysis.
- Root-cause analysis took minutes instead of weeks or months.
- Improved transparency and confidence in liquidity data.
By combining AI engineering with regulatory reporting expertise, Sia helps banks understand and trust their data.