Bank Data Warehouse Design

The Challenge

The bank's transactional data was scattered across core banking, card processing, and CRM systems with no unified model. Regulatory reporting was a manual, error-prone exercise that took several days each quarter, and fraud analytics teams were working from extracts that were already stale by the time they landed.

The Approach

The priority was a conformed model that finance, risk, and compliance teams could all rely on — refreshed nightly instead of rebuilt by hand each quarter.

• Designed a conformed star schema covering accounts, transactions, customers, and risk dimensions.
• Built Pentaho ETL jobs to extract and conform data nightly from core banking and CRM sources.
• Layered SSAS OLAP cubes with MDX calculations so finance and risk teams could self-serve their own analysis.
• Implemented slowly changing dimensions so customer 360 views stayed accurate through account and product changes over time.

The Results

Regulatory reporting moved from a quarterly fire drill to a routine nightly process, and fraud analytics finally had a current, trustworthy dataset to work from.

Key Takeaways

In a regulated environment, the data model matters as much as the pipeline. Getting the conformed dimensions and slowly changing history right up front meant compliance and fraud teams could finally trust the numbers instead of reconciling spreadsheets every quarter.