Compliance analysts spent two weeks each quarter manually pulling and formatting data from six source systems for regulatory submissions. An AI-assisted ADF pipeline reduced that to two days with a full audit trail.
Quarterly regulatory submissions required data from six different systems — core banking, loan origination, risk management, GL, CRM, and a third-party data provider. Each system had different formats, and the manual process was error-prone and audit-unfriendly. A single missed field or calculation error could trigger a regulatory inquiry.
Used Azure Purview to catalog all six source systems and Copilot for Data Factory to generate initial mapping suggestions between source fields and regulatory submission format requirements.
Developed parameterized Azure Data Factory pipelines for each source system, with dynamic configuration allowing the same pipeline framework to handle different regulatory submission formats by changing parameters.
Encoded all regulatory calculation rules (exposure calculations, risk weightings, aggregation logic) as reusable Power Query functions and SQL Server stored procedures — documented and version-controlled.
Built automated reconciliation steps that compared extracted totals against source system control totals, flagging any discrepancies before data was included in the submission file.
Configured the pipeline to automatically generate submission-ready files in the required format, along with a complete audit log documenting every transformation applied to every record.
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