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Unified data infrastructure for risk management in finance

Data & Analytics Platform & Cloud Financial Services Retail, Consumer & Hospitality
Data infrastructure for finance risk management Data infrastructure for finance risk management

Client

Our client is the UK’s leading price comparison website, helping millions find better deals on insurance, utilities, credit cards, and more. The platform uses real-time data to deliver accurate, personalized results tailored to each user. 

It processes massive amounts of information daily, which requires dependable and automated data systems. For the client, data integrity is essential to ensure every offer is timely and pertinent and can be relied on by users. 
max-belov-cto-coherent-solutions

Max Belov

CTO at Coherent Solutions

This technology is transforming industries like manufacturing, healthcare, and autonomous vehicles by enabling real-time analytics and decision-making, enhancing efficiency and responsiveness.

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Business Challenge

The client turned to us to solve two pressing challenges:

  1. Broken data pipelines prevented consistency and scalability across internal teams. 

  2. Manual processing bottlenecks took up to 20 hours each month, slowed operations, and increased reporting turnaround times.

  3. Building a dynamic orchestration layer that helps integrate legacy systems with modern cloud-native components.

The client needed an effective, automated, and scalable solution to derive value from data without magnifying manual effort.

1.

UK-based high-traffic consumer portal

2.

Data bottlenecks prevented scalability

3.

35% improvement in data processing time

Solution

We created and delivered a fully automated data orchestration and reporting platform customized as per the client's requirements. 

Key Steps & Methodology

Phase 1. Analysis & Design 

Our team completed a comprehensive review of existing data processes. They identified inefficiencies and designed a scalable architecture using Airflow and GCP-native services. 

Phase 2. Development & Testing 

During this phase, our engineers:

  • Implemented modular DAGs in Apache Airflow for orchestration

  • Employed DBT to transform data and BigQuery for centralized analytics
  • Used Terraform for Infrastructure-as-Code and GCP Registry for managing dockerized components
  • Implemented strong error handling and rollback mechanisms 

Phase 3. Production Release & Optimization

We successfully implemented ongoing improvements via monitoring, observability dashboards, and auto-scaling, including key features such as:

  • Data flow automation. Fully automated pipelines manage ingestion, transformation, and delivery. 

  • Enhanced accessibility. Cleaned and structured data is available in real time to downstream systems. 

  • Scalability. A modular, cloud-native setup makes future feature expansion easy and low-cost. 

  • Visibility and control. Fintech teams get greater visibility into data performance, lineage, and quality. 

Results

This partnership led to the restructuring of complicated, siloed data processes into a lean and visible system. The client now makes quicker, better decisions on a scale.  

Our work delivered tangible results: 

  • Saved 20+ hours/month of manual effort 

  • Improved data processing time by more than 35%  

  • Improved data reliability and quality, which positively impacted strategic reporting 

  • Facilitated quicker experimentation cycles for internal business analysts 

With clarity and speed brought to their data processes, the client now owns a strong foundation for smarter decision-making across the business and control over financial risk.