What challenges did Starling face before AI implementation?

Starling needed to overcome bottlenecks in its previous data and ML infrastructure to support growing AI/ML initiatives. The bank aimed to enhance fraud detection, automate customer service, and provide intuitive financial insights to customers. There was a need for a secure, scalable platform to support AI innovation and risk management in a regulated banking environment.

What was the impact of the AI solution?

Significant increase in scam payment cancellations, improving customer protection. Saved thousands of manual hours through AI-driven automation of customer support workflows. Halved customer service referrals by providing AI-powered accurate help center responses. Accelerated ML innovation and empowered client banks globally to deploy generative AI safely and effectively.

Source

GCPProductionEvidence: Medium55/100

Starling Innovates Customer Banking Experience with Google Cloud Generative AI

Use case typeFraud detectionUpdated Jun 13, 2026

Starling, a UK digital bank, enhanced customer financial management, fraud detection, and customer support by leveraging Google Cloud AI technologies including Vertex AI, BigQuery, Gemini, and Model Armor. The bank migrated its data warehouse to BigQuery and developed AI features such as Scam Intelligence, Spending Intelligence, and automated call transcript summarization using Gemini multimodal models and Vertex AI pipelines. Starling offers its capabilities to other banks via the Engine by Starling platform, enabling safe, scalable generative AI deployments for improved banking services globally.

Organization: Starling
Industry: Finance
Location: United Kingdom
Published: May 2026

Reported outcomes

Strategic outcomes

Customer experience & trustImproved scam payment protectionNew product / capabilityIntroduced natural-language spending insightsSpeed & agilityAutomated customer support workflowsEcosystem & partnershipsEnabled safe AI deployment for other banks

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Starling, Engine by Starling
Provider: GCP
Maturity: Production
Linked source: Google Cloud Customer Stories

Engine by Starling platform provides other banks with modern cloud-native core banking capabilities and AI deployment tooling with compliance and operational safety

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Fraud detection
2Customer support automation
3Financial insights

Starling needed to overcome bottlenecks in its previous data and ML infrastructure to support growing AI/ML initiatives.
The bank aimed to enhance fraud detection, automate customer service, and provide intuitive financial insights to customers.
There was a need for a secure, scalable platform to support AI innovation and risk management in a regulated banking environment.

Migrated data warehousing to Google Cloud BigQuery and adopted Vertex AI for secure model development and deployment.
Built the Scam Intelligence tool using Gemini multimodal AI to detect suspicious marketplace ads, increasing scam payment cancellations by 300%.
Developed Spending Intelligence using Gemini for natural language financial queries.
Implemented AI-powered automated call transcript summarization saving 8,000 hours per month.
Engine by Starling platform provides other banks with modern cloud-native core banking capabilities and AI deployment tooling with compliance and operational safety.

Technologies

Vertex AI BigQuery Gemini Model Armor

Significant increase in scam payment cancellations, improving customer protection.
Saved thousands of manual hours through AI-driven automation of customer support workflows.
Halved customer service referrals by providing AI-powered accurate help center responses.
Accelerated ML innovation and empowered client banks globally to deploy generative AI safely and effectively.

Sources & evidence1

Evidence: Medium55/100Evidence strength