The Challenge
With the advent of Open Banking, financial institutions are tasked with harnessing diverse data sources to drive competitive advantage in the fintech space. A leading financial institution, eager to leverage this opportunity, faced the daunting challenge of aggregating data from 14 distinct sources. These sources were varied, encompassing transactional data from legacy banking systems, customer interactions, third-party fintech API feeds, and regulatory compliance data. The existing data infrastructure was disparate and unable to provide a comprehensive view, essential for real-time analytics and insight generation.
The bank needed a transformative approach to integrate and standardise these numerous data streams into a singular analytics layer capable of supporting strategic decision-making and operational efficiency. Ensuring data security and compliance with strict financial regulations added further complexity to the project.
The Solution
By deploying its expertise in fintech and data engineering, Adyantrix delivered a robust solution. Our team meticulously crafted a unified data pipeline architecture designed to ingest, clean, and standardise data from the 14 diverse sources. Utilising cloud-native technologies and advanced API integration techniques, we developed a scalable and secure platform to support ongoing data aggregation.
The heart of our solution was a centralised analytics layer that consolidated data into a structured format. Leveraging state-of-the-art data normalisation processes, we ensured seamless data transformation and integration. This enabled dynamic dashboards and real-time reporting, vital for quick and informed decision-making.
Our inclusion of automated compliance management workflows integrated directly into the data pipeline ensured all data flows adhered to financial regulations and industry standards, thus maintaining strict security and integrity protocols.
Key Results
By implementing this comprehensive solution, the financial institution achieved several key outcomes:
- Increased Efficiency: Data processing time was reduced by 45%, enabling the institution to focus resources on analysis rather than data collection.
- Enhanced Insights: The unified analytics layer provided a holistic view of customer behaviour, leading to a 30% improvement in customer acquisition strategies.
- Real-Time Decision Making: The capacity for instant access to integrated data streams facilitated agile decision-making, culminating in a 20% increase in operational productivity.
- Compliance Assurance: Automated workflows improved regulatory compliance reporting efficiency by 40%, minimising potential risk exposures.
In essence, Adyantrix's solution delivered a future-proof data architecture, setting a new standard for how financial institutions can effectively harness the power of Open Banking to unlock unprecedented insights and competitive advantage.
Technical Approach
The pipeline was built on a cloud-native stack on Google Cloud Platform (GCP), selected for its native support for financial-grade data security controls and its managed streaming capabilities. The architecture comprised four primary layers:
Ingestion layer: Apache Kafka (managed via Confluent Cloud) handled all real-time data streams from the Open Banking API feeds, providing durable message queuing with exactly-once delivery semantics — a critical requirement when processing financial transactions where duplicate or missing records carry regulatory consequences. Batch ingestion from the four legacy core banking systems (running on IBM Db2 and Teradata) was handled via Google Cloud Dataflow pipelines running on scheduled intervals, with change-data-capture (CDC) using Debezium to minimise load on production databases.
Transformation layer: dbt (data build tool) was used for all SQL-based transformations within Google BigQuery, providing version-controlled, tested transformation logic that the bank's internal data team could maintain and audit independently. Over 180 dbt models were built, covering customer entity resolution, transaction categorisation (using a custom taxonomy aligned to the FCA's CASS rules), and the calculation of derived financial health metrics used by the recommendation engine.
Analytics and ML layer: BigQuery ML was used to train and serve the personalised product recommendation models, keeping all model data within the same governed environment as the underlying transactional data rather than exporting to a separate ML platform. This simplified the data residency compliance posture significantly.
Security and compliance layer: All data in transit was encrypted using TLS 1.3; all data at rest used AES-256 encryption with customer-managed keys held in Google Cloud KMS. Data lineage tracking was implemented using Dataplex, providing an auditable record of every transformation applied to any piece of customer data — a requirement under both GDPR Article 30 (records of processing activities) and the FCA's data governance expectations.
Implementation Highlights
The engagement ran over 20 weeks, with a dedicated data security review embedded within the delivery team rather than being treated as a final gate.
Source system assessment: Each of the 14 sources was assessed against a standardised data quality scorecard covering completeness, timeliness, consistency, and schema stability. Four sources — including two third-party Open Banking aggregator APIs — had undocumented schema changes in the previous 12 months that had caused downstream data corruption in the client's existing reporting. Understanding this instability upfront meant we built schema validation checks at the ingestion point for those four sources specifically, alerting the operations team immediately rather than allowing corrupt data to propagate silently.
Customer entity resolution: The most technically involved component was building a unified customer view across 14 sources that used different customer identifiers, different name formats, and different address standards. We implemented a probabilistic entity matching algorithm using a combination of deterministic rules (matching on National Insurance numbers and sort code / account number pairs where available) and fuzzy matching using Jaro-Winkler similarity scoring for name and address fields, achieving a 97.3% match rate on a ground-truth test set of 10,000 known customer relationships.
Regulatory compliance automation: The pipeline included automated generation of Consumer Duty outcome monitoring reports (required under FCA PS22/9), drawing directly from the unified analytics layer rather than from manual data pulls. This reduced the compliance team's monthly reporting effort from approximately 120 hours to under 20 hours, freeing substantial analytical capacity.
Recommendation engine deployment: The product recommendation models were A/B tested through the bank's digital banking portal over a six-week period before full deployment, using a multi-armed bandit framework to allocate traffic between the new personalised recommendations and the existing rule-based suggestion engine. The personalised engine outperformed the rule-based system on click-through rate by 34% and on product take-up by 18% within the test period, providing clear statistical justification for full deployment.
Measurable Outcomes
The quantitative improvements extended across operational, commercial, and compliance dimensions:
- The 45% reduction in data processing time was measured as end-to-end pipeline latency — from a transaction occurring in the source system to the event being queryable in the analytics layer. The previous average was 18 hours (due to overnight batch processing); the new pipeline reduced this to under 10 minutes for real-time sources and under 4 hours for the legacy batch sources, fundamentally changing the business's ability to act on customer behaviour signals.
- The 30% improvement in customer acquisition strategies was measured by comparing new product take-up rates in the six months following the recommendation engine launch against the equivalent prior-year period, adjusted for market seasonality.
- Regulatory reporting efficiency improved by 40% as measured by compliance team time spent on monthly FCA returns — a saving of approximately 100 analyst-hours per month with a meaningful reduction in the risk of manual error in regulatory submissions.
- The total cost of ownership of the new platform was calculated at 28% lower than the legacy data infrastructure it replaced, when factoring in the decommissioning of four on-premises reporting servers and the elimination of three third-party data aggregation licences that had become redundant once the unified pipeline was operational.
Why This Approach Worked
The architecture succeeded because it was designed around the bank's specific regulatory operating environment rather than being a generic data platform adapted for financial services. Three decisions were particularly consequential:
First, choosing dbt for transformation logic rather than custom Python scripts made the transformation layer auditable and maintainable by the bank's own data team. Every transformation is a documented, version-controlled SQL model with built-in data quality tests — a far more defensible posture for a regulated entity than opaque custom code.
Second, keeping the ML models within BigQuery ML rather than exporting data to a separate platform eliminated a significant class of data governance complexity. The recommendation models train and serve on data that never leaves the governed BigQuery environment, meaning every model decision can be traced back to source data that sits within the bank's own compliance perimeter.
Third, the embedded compliance engineer in the delivery team — rather than a compliance review at the end — meant that regulatory requirements shaped architecture decisions from week one. The Dataplex data lineage implementation, the customer-managed encryption keys, and the Consumer Duty reporting automation were all designed in from the start, not retrofitted. In financial services, compliance as an afterthought is not just expensive to fix — it is a project risk that can halt delivery entirely.
Speak with our Data Engineering team at Adyantrix to find out how we can support your next project.
Work with Adyantrix
If you are looking to tackle a similar challenge, Adyantrix has the expertise to help across the full project lifecycle. Our data engineering practice covers pipeline design, streaming, and data infrastructure. Our data analytics practice covers BI reporting and self-serve analytics platforms. Our analytics & insights practice covers BI dashboards and exploratory analysis. Get in touch to discuss your requirements — no commitment required.
