Modernising FSCS Regulatory Reporting
with Big Data
Using Big Data to modernise FSCS regulatory reporting within one of the UK’s largest banking groups.
The Client
One of the most relevant banking institutions in the UK and Europe, operating in a highly regulated financial environment and subject to strict compliance requirements, including mandatory adherence to the Financial Services Compensation Scheme (FSCS) and reporting obligations to the Prudential Regulation Authority (PRA).
The solution was delivered within the bank's Data Innovation area, a large-scale engineering organisation of approximately 80 data professionals, responsible for driving enterprise Big Data transformation and regulatory modernisation programmes.
The solution was delivered within the bank's Data Innovation area, a large-scale engineering organisation of approximately 80 data professionals, responsible for driving enterprise Big Data transformation and regulatory modernisation programmes.
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Customers world wide
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UK Customers in the UK
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Banks across 3 licences in UK
The Challenge
Compliance with the Financial Services Compensation Scheme (FSCS) is mandatory for UK banks, requiring the accurate calculation of protected deposits and on-demand reporting to the Prudential Regulation Authority (PRA).
The existing data warehouse-based process required up to 72 hours to generate regulatory reports, limiting responsiveness and preventing daily execution. In addition, the institution operated under multiple banking licences and legal entities, meaning customer data was fragmented across different systems.
For FSCS purposes, compensation must be calculated per unique customer, not per account or per entity. This required consolidating and reconciling high-volume data to identify the same customer across licences and banks, creating a unified and accurate depositor view before applying regulatory calculations.
The objective was clear: implement a scalable Big Data solution capable of handling complex multi-entity consolidation, improving performance and SLAs, and industrialising the delivery of future regulatory reporting initiatives.
The existing data warehouse-based process required up to 72 hours to generate regulatory reports, limiting responsiveness and preventing daily execution. In addition, the institution operated under multiple banking licences and legal entities, meaning customer data was fragmented across different systems.
For FSCS purposes, compensation must be calculated per unique customer, not per account or per entity. This required consolidating and reconciling high-volume data to identify the same customer across licences and banks, creating a unified and accurate depositor view before applying regulatory calculations.
The objective was clear: implement a scalable Big Data solution capable of handling complex multi-entity consolidation, improving performance and SLAs, and industrialising the delivery of future regulatory reporting initiatives.
Our Solution
A production-grade Big Data regulatory platform enabling compliant, high-performance
FSCS reporting and industrialised Spark delivery across the Data Innovation area.
FSCS reporting and industrialised Spark delivery across the Data Innovation area.
Big Data Architecture
Implemented a distributed Big Data platform on a 20-node Cloudera Hadoop cluster, using Spark and Scala to optimise parallel processing of billions of records. Engineered for high-volume batch execution, performance tuning, and production-grade regulatory SLAs.
Customer Consolidation
Consolidated fragmented data across 3 licences and 6 banking entities, identifying unique customers across all entities. Built a unified depositor view to ensure accurate FSCS compensation calculations per individual.
ETL Delivery Framework
Delivered a metadata-driven Spark framework to standardise and industrialise ETL development. Enabled reusable transformation patterns, faster delivery cycles, and consistent implementation across Agile teams.
Reporting Framework
Built a metadata-driven reporting framework to generate regulatory datasets in multiple formats and distribute them through different interfaces. Enabled automated, repeatable FSCS reporting with strong reuse capabilities.
Platform Migration
Migrated regulatory transformations from the legacy data warehouse to the Big Data platform, ensuring controlled coexistence during validation. Re-engineered data flows for improved scalability and performance.
Agile Delivery Framework
Established an industrialised Spark delivery framework including CI/CD, automated testing, DevOps practices, and controlled multi-environment deployments.
The Value
The Modern Data Platform transformed how business teams access, trust, and act on data—enabling faster decisions, stronger governance, and scalable growth.
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Reporting time optimisation
Reduced FSCS reporting from 72 hours to under 8 hours, enabling daily execution.
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Records processed per run
Processed billions of records on a 20-node Hadoop cluster for multi-entity regulatory calculations.
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Engineers using the framework
Framework adopted across the Data Innovation area, standardising Spark delivery and reporting at scale.
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Daily Regulatory Readiness
Reduced the end-to-end FSCS reporting process from approximately three days to under eight hours, enabling reliable daily execution. The bank moved from constrained batch reporting to operational regulatory readiness with improved SLAs and responsiveness to PRA requirements.
Accurate Multi-Entity Consolidation
Enabled consistent identification of unique customers across licences and banking entities, ensuring compensation calculations were performed correctly per depositor. Delivered higher confidence in regulatory accuracy under complex multi-entity conditions.
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Industrialised Big Data Delivery
Established metadata-driven frameworks and an end-to-end Spark delivery model that standardised development, reduced implementation risk, and accelerated future regulatory and ETL initiatives. The approach became a reusable reference within the Data Innovation area.
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