What challenges did Intesa Sanpaolo face before AI implementation?

On-premise lab environment separated from production, delaying risk management solution deployment. Inability to scale model development in sequence, causing slow time to market. Need to improve real-time risk oversight and regulatory reporting compliance.

What was the impact of the AI solution?

Reduced time for mandatory EU stress-tests by over 80%. Decreased ML model development time by 30%. Enabled parallel development of risk models, accelerating release. Improved real-time risk management visibility and regulatory compliance.

Source

GCPEvidence: Medium50/100

Intesa Sanpaolo - Fast and Unified Financial Risk Management with Google Cloud AI

Use case typeRisk assessmentUpdated Jun 13, 2026

Intesa Sanpaolo faced challenges in accelerating risk management solution development due to separate lab and production environments and slow model development. They built a Democratic Data Lab on Google Cloud using Vertex AI, Gemini, BigQuery, Google Kubernetes Engine, and Looker to unify environments and enable parallel development. This approach reduced regulatory stress test completion time by over 80%, cut model development by 30%, and enhanced real-time risk reporting and regulatory compliance. Looker dashboards enabled real-time risk visibility for executives, improving decision-making and proactive risk mitigation.

Organization: Intesa Sanpaolo
Industry: Finance
Location: Italy
Published: January 2026

Reported outcomes

−30%

timeTime & speed

−80%time

Strategic outcomes

Risk & complianceImproved regulatory risk complianceBetter decisions & insightImproved real-time risk visibilitySpeed & agilityEnabled parallel risk model developmentNew product / capabilityUnified lab and production environments

Catalog median for time & speed deployments: −50% across 312 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 80% decrease

Google Cloud Customer StoriesCustomer storyInferred claimMedium evidence strength

Reduced time for mandatory EU stress-tests by over 80%.

Normalized claim

Time: 30% decrease

Google Cloud Customer StoriesCustomer storyInferred claimMedium evidence strength

Decreased ML model development time by 30%.

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

Customer: Intesa Sanpaolo
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Customer Stories

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Risk Management
2Machine Learning
3Data Analytics

On-premise lab environment separated from production, delaying risk management solution deployment.
Inability to scale model development in sequence, causing slow time to market.
Need to improve real-time risk oversight and regulatory reporting compliance.

Built a cloud-based Democratic Data Lab on Google Cloud to unify lab and production environments eliminating the rewrite requirement.
Leveraged Vertex AI and Gemini for ML model development and automated data extraction.
Used BigQuery as data warehouse and Google Kubernetes Engine for resource scaling.
Implemented Looker dashboards for real-time visualization of risk metrics across organization.

Technologies

Google Cloud Vertex AI Gemini BigQuery Google Kubernetes Engine Looker

Reduced time for mandatory EU stress-tests by over 80%.
Decreased ML model development time by 30%.
Enabled parallel development of risk models, accelerating release.
Improved real-time risk management visibility and regulatory compliance.

Architecture

The architecture features a unified cloud-based lab-integrated production environment using Google Kubernetes Engine for scaling, BigQuery as the data warehouse, Vertex AI and Gemini for AI model development, and Looker dashboards for real-time reporting across risk types.

Sources & evidence1

Evidence: Medium50/100Evidence strength