Google Cloud Financial Services UK

Lloyds Banking Group

System Migration

To meet these changing expectations, banks need to combine powerful data analytics with scalable machine learning (ML) capabilities to help customers make the most of their money. The wealth of customer data in Lloyds Banking Group's hands puts it in a strong position to understand its customers and meet their needs.

Read the original story Explore all use cases

Gemini platform has a much lower cost of entry, allowing us to prototype quickly, while giving us the scalability to deliver new use cases at pace.

Reduced the income verification step in its customers' mortgage applications from days to seconds

Work is also ongoing on a cutting-edge Agentic AI system which will transform the ways customers interact with the Bank and provide a more personalized experience for end users.

Reported impact

Business outcome

Work is also ongoing on a cutting-edge Agentic AI system which will transform the ways customers interact with the Bank and provide a more personalized experience for end users. The prototype was built in collaboration with Google Cloud and is expected to launch to customers later this year.

Solution

Lloyds Banking Group adopted Gemini platform to leverage its customer data and gain insights to meet evolving expectations of business and retail customers. "Our aim was to move onto a platform that enabled machine learning innovation and experimentation at scale," explains Arshad Ahmed, Head of ML Product at Lloyds Banking Group. "We needed a cloud platform to allow us to experiment with ML and meet our customer expectations. We needed a scalable cloud platform to allow us to experiment with ML and meet our customer expectations. Lloyds Banking Group migrated to Gemini platform (formerly Vertex AI) in 2024 and it now has over 300 data scientists and AI developers across the Group using the platform. Unplanned platform downtime is now zero, while managed Google Cloud services allow its data scientists to supercharge their experimenting. In the space of just six months, the bank has developed 80 new ML experiments on the platform.

Solution architecture

6 components · 4 layers

Ingest
- Pub/Sub Captures and distributes event streams between upstream systems and downstream processing services.
Compute
- Google Kubernetes Engine (GKE) Runs containerized or virtualized application workloads with the elasticity needed for production scale.
- Dataflow Processes and transforms batch or streaming data for downstream analytics and AI.
Storage
- BigQuery Serves as the governed analytics and storage layer for high-volume operational data, features, and reporting.
- Cloud Storage Stores source documents, media, or raw files that feed the automated workflow.
Orchestration
- Gemini platform Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.

Architecture clues

Work is also ongoing on a cutting-edge Agentic AI system which will transform the ways customers interact with the Bank and provide a more personalized experience for end users.

Evidence from the source

"Our aim was to move onto a platform that enabled machine learning innovation and experimentation at scale," explains Arshad Ahmed, Head of ML Product at Lloyds Banking Group. "We needed a cloud platform to allow us to e

For Charlotte Nickels, Head of Transaction Classification Data at Lloyds Banking Group, the difference is transformative. "Our previous platform didn't allow us to experiment at scale," she explains. "Now, with Gemini pl

Lloyds Banking Group adopted Gemini platform to leverage its customer data and gain insights to meet evolving expectations of business and retail customers.

Lloyds Banking Group migrated to Gemini platform (formerly Vertex AI) in 2024 and it now has over 300 data scientists and AI developers across the Group using the platform. Unplanned platform downtime is now zero, while

We needed a scalable cloud platform to allow us to experiment with ML and meet our customer expectations. Gemini platform gave us exactly that.

Transparency Evidence & methodology 61/100 reference value · 75% confidence · 1 source

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

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 value61/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth15/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity16/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact0/25
0 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence75%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
5 direct source quotes retained.
Date provenance0/10
The publication date has no documented provenance.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity6/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

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

Google Cloud
Lloyds Banking Group customer story

First-party
https://cloud.google.com/customers/lloydsbankinggroup

Source type
Vendor-published customer story
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes

Date evidence

First archived Dec 14, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
Open date source
Sitemap last modified Oct 8, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Similar use cases

See all like this

Google Cloud Archived · Oct 2, 2026 M Lhuillier Financial Services Philippines The company needed to move away from legacy on-premise hardware and previous cloud environments that suffered from high maintenance costs, scalability bottlenecks during peak holiday remittance periods, and slow… Accelerated 2-year cloud transition into an 8-month rollout IT Operations AutomationReal-time Analytics BigQuery · Cloud Run · Gemini

Google Cloud Archived · Apr 28, 2026 CME Group Financial Services United States CME Group the world’s leading derivatives marketplace, has built an enviable reputation for trust and reliability over its 175-year history. However, driven by a culture of innovation, it isn’t resting on its legacy and… 10+ hours of monthly productivity gains per developer Application RefactoringSystem Migration Antigravity · Gemini CLI · Gemini Code Assist

Google Cloud Archived · Jun 11, 2026 InMobi Software / SaaS India InMobi needed to migrate a latency-sensitive advertising platform from another hyperscaler, preserve zero-downtime service at massive QPS, and create a more efficient real-time serving and analytics environment for both… 7 million peak qps Application RefactoringReal-time Analytics Bigtable · Cloud Composer · Cloud SQL