Databricks Financial Services United Kingdom

Oakbrook

Customer Service Automation · Enterprise Knowledge Search

Stringent regulation and outdated approaches to consumer lending have made it more difficult for many consumers across the UK to obtain loans. However, fintech organizations such as Oakbrook Finance are revolutionizing the lending sector, bringing more agile decision-making driven by advanced data science models and analytics.

Indeed, the company has been able to launch two new products over the last 12 months, as well as a top-up loan and collections decision engine.
Over the first three years, the company projects that they will realize cost savings of 14% through productivity gains and optimized compute.
Projected cost savings over the first 3 years
Revenue increase forecast over the next 12 months from new top-up loans

Reported impact

Business outcome

Oakbrook now has a platform that is faster, scalable, more reliable, and secure, leading to considerable cost efficiencies and valuable new business use cases. Over the first three years, the company projects that they will realize cost savings of 14% through productivity gains and optimized compute.

  • Cost Reduction
  • Productivity Improvement
  • Revenue Growth

Solution

Oakbrook built its Databricks implementation around Delta Lake, Unity Catalog, Databricks Data + AI Platform, BigQuery. Stringent regulation and outdated approaches to consumer lending have made it more difficult for many consumers across the UK to obtain loans. However, fintech organizations such as Oakbrook Finance are revolutionizing the lending sector, bringing more agile decision-making driven by advanced data science models and analytics. Delta Lake stores curated lakehouse tables with ACID guarantees for downstream analytics and machine learning. Unity Catalog applies centralized governance, lineage, auditing, and access control across shared data assets. Databricks Data + AI Platform provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment. BigQuery acts as a remote analytics source queried alongside Databricks data.

Data flow

Source data and events are ingested through managed connectors, files, or streaming feeds into databricks, curated in Delta Lake tables, processed with Databricks compute and orchestration services, and exposed to downstream analytics and users. It connects with BigQuery.

Solution architecture

4 components · 4 layers
  1. Ingest
    • BigQuery Acts as a remote analytics source queried alongside Databricks data.
  2. Storage
    • Delta Lake Stores curated lakehouse tables with ACID guarantees for downstream analytics and machine learning.
  3. Governance
    • Unity Catalog Applies centralized governance, lineage, auditing, and access control across shared data assets.
  4. Uncategorized
    • Databricks Data + AI Platform Provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment.

Architecture clues

  • A unified environment for increased efficiency, collaboration and productivity
  • BigQuery
  • Databricks Data + AI Platform
  • Delta Lake
  • Lack of scalability, storage and flexibility hindered growth
  • Unity Catalog

Evidence from the source

Ed Ball, Head of Data, Security and Service Management at Oakbrook, summarized: “The lakehouse provides a great experience for our data engineers and data scientists, and by constant development of our data and analytics platform, we’ll con
Oakbrook’s products are designed with flexibility, simplicity and positive customer experiences in mind, and are underpinned by their proprietary loan application and decisioning platform, known as “O6K.” Customers can access the company’s
Over the first three years, the company projects that they will realize cost savings of 14% through productivity gains and optimized compute.
Projected cost savings over the first 3 years
Revenue increase forecast over the next 12 months from new top-up loans
The organization has in excess of 100,000 customers and has offered £1.1B in loans since launch.
Transparency Evidence & methodology 100/100 reference value · 99% confidence · 3 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence99%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    3 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Databricks

    Oakbrook customer story

    First-party
    https://www.databricks.com/customers/oakbrook
    Source type
    Vendor-published customer story
    Published
    Aug 30, 2023
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. Companies House

    OAKBROOK FINANCE LIMITED overview - Find and update company information - GOV.UK

    Government
    https://find-and-update.company-information.service.gov.uk/company/07831517

    Companies House lists the registered office address as 'The Poynt North, 45 Wollaton Street, Nottingham, United Kingdom, NG1 5FW.'

    Source type
    Government registry
    Supports
    • Region
  3. GlobalDatabase

    OAKBROOK FINANCE LIMITED company key information - UK.GlobalDatabase.com

    Independent secondary
    https://uk.globaldatabase.com/company/oakbrook-finance-limited

    GlobalDatabase says 'OAKBROOK FINANCE LIMITED currently employs 39 people' and notes a staff decrease versus the previous year; fewer than 250 employees maps to SMB.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • Published on source page Aug 30, 2023 · Databricks · First-party
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.