As companies expand across regions, products and digital channels, the volume of data they generate grows exponentially. Managing and consolidating that data across fragmented and highly distributed systems presents a major challenge. General ledger consolidation – the process of combining financial information from multiple systems and business functions into a single, consistent reporting view – is one area where this complexity is particularly evident within modern banking environments.
While core platforms, financial administration systems, loan management environments, digital channels and supporting reference systems each serve critical operational functions, consolidating information across them into a single, trusted financial view can be very difficult. Data structures differ between systems. Reporting standards and business rules vary. Information exists at different levels of granularity. Historical records are often distributed across multiple operational and archival platforms. Traditional consolidation approaches frequently rely on manual intervention, spreadsheet-based processing and static data extracts, creating inefficiencies that cannot readily be managed at scale.
These challenges become even more pronounced when accurate point-in-time reporting is required. To address this growing complexity, financial institutions require a solution that supports more scalable, governed and efficient general ledger consolidation across complex, distributed data environments. This use case examines how Fraxses was utilised to support point-in-time general ledger consolidation within a major European retail bank’s data environment.
As is typical of large organisations, the bank’s data was distributed across several different systems and repositories. A financial administration system maintained information primarily at an aggregated ledger level, while multiple product systems contained transactional and customer-level data at a far more granular level. Supporting reference information resided across separate systems responsible for maintaining product, customer and metadata structures.
Reconciling and consolidating this data was difficult not only because it was distributed across multiple systems, but also because those systems operated at different levels of detail and served different operational purposes.
As data volumes increased across the retail bank, traditional approaches to ledger consolidation became highly resource-intensive. Large-scale movement of data between systems created processing overhead, while static ETL-based approaches reduced flexibility and increased operational complexity. Maintaining consistent consolidation logic across reporting periods also became progressively more difficult as the bank’s data environment evolved. At the heart of the challenge, however, was the difficulty of historical data alignment.
Financial and operational data within enterprise banking environments typically exists in both current and historical forms. Source systems retain recent historical records within operational environments, with older information often residing in archival platforms or supporting repositories.
To support accurate reporting, records commonly contain validity date ranges that define the period during which a particular record is considered valid. Active records may contain open-ended date ranges, while historical records must be aligned across multiple systems to ensure consistency for a specific reporting period. Point-in-time reporting, which requires information to be retrieved exactly as it existed at a specific reporting date regardless of subsequent changes made within source systems, creates significant complexity when retrieving financial data as it existed at a given point in time.
When reconciling ledger information for a specific reporting date, valid records must be selected consistently across all participating systems before joins and aggregations can be applied. As the number of source systems, tables and reporting rules increases, it becomes more difficult to maintain the logic required to support this process using traditional approaches.
Historically, many ledger consolidation processes addressed this by prompting users to specify reporting dates that were then passed through multiple subqueries across separate datasets before the resulting information was joined. While workable at smaller scale, this kind of approach proved too resource-intensive and difficult to govern as environments grew.
The bank therefore needed a new solution capable of supporting scalable point-in-time ledger consolidation while reducing unnecessary data movement and improving consistency across reporting periods.
To address these challenges, Fraxses was implemented as a governed consolidation and integration layer capable of connecting and processing data across the bank’s distributed systems.
A three-tier operational model aligned to Medallion Architecture principles was implemented to support the progressive refinement of Data Objects across Bronze, Silver and Gold data layers within the bank’s data environment.
At the source level, Fraxses connected directly to the bank’s financial administration systems, product systems and supporting reference repositories. Rather than relying exclusively on large-scale data extraction and movement, query pushdown techniques were used to apply filters and processing logic closer to the source systems, reducing unnecessary data transfer and improving processing efficiency.
Reusable Data Objects played a key role in the implementation by centralising consolidation logic and supporting consistent point-in-time filtering across reporting periods. These governed structures enabled the accurate retrieval and joining of ledger entries across multiple systems while maintaining alignment between valid historical records. By encapsulating this logic within reusable governed structures, the need to recreate consolidation rules for different reporting cycles was greatly reduced.
Fraxses also leveraged Cache and Snapshot capabilities to support improved scalability and reduce reliance on traditional ETL processes. This enabled frequently used consolidation datasets and historical states to be retained and reused efficiently, improving performance across large-scale consolidation operations. The result was a more governed and sustainable approach to ledger consolidation across the bank’s distributed systems.
The implementation of Fraxses delivered several key operational improvements:
By shifting towards a more governed, reusable and distributed consolidation model, the bank established a more efficient framework for supporting financial visibility across its broader systems landscape.
Springboard for Broader Finance Transformation
This project demonstrated how a governed consolidation and integration layer can become a springboard for wider finance modernisation across complex, multinational banking environments. For CFOs and finance leaders, this creates the opportunity to derive further value across a number of strategic areas:
The success of this initial implementation prompted broader adoption of the platform within the retail bank, where Fraxses continues to play an integral operational role today.
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