Intesa Sanpaolo: Managing evolving financial risk at speed with data analytics and AI
Customer Company Size
Large Corporate
Region
- Europe
Country
- Italy
Product
- Google Cloud
- BigQuery
- Looker
- Vertex AI
- Google Kubernetes Engine
Tech Stack
- Data Analytics
- AI
- Cloud Computing
- Machine Learning
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
- Digital Expertise
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Functions
- Business Operation
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
- System Integration
About The Customer
Intesa Sanpaolo is a leading financial services institution in Italy, excelling in retail and corporate banking, as well as wealth management. The Intesa Sanpaolo Group serves 13.7 million customers through a network of over 3,300 branches, strategically distributed across the country. The bank holds market shares no lower than 12% in most Italian regions, showcasing its strong presence and influence in the financial sector. As a large corporate entity, Intesa Sanpaolo is committed to innovation and efficiency, particularly in the realm of risk management. The bank's focus on adopting advanced technologies like data analytics and AI reflects its dedication to maintaining a competitive edge in the rapidly evolving financial landscape. By leveraging these technologies, Intesa Sanpaolo aims to enhance its risk management capabilities, ensuring compliance with regulatory requirements and effectively mitigating financial risks.
The Challenge
With its on-premise data analytics lab, Intesa Sanpaolo’s risk management team found it challenging to keep pace with a rapidly evolving financial risk landscape. The global banking system presents significant risk management challenges for financial services institutions due to complex financial structures and sophisticated trading algorithms. Risk management teams need to adopt a data-driven approach to identify and mitigate risks and comply with regulatory requirements. Previously, Intesa Sanpaolo’s risk management team used an on-premise data analytics lab to prototype machine-learning solutions and risk models. However, this lab environment was separate from the production environment, causing delays in releasing solutions and making it harder for the bank to react quickly to changing markets. Additionally, the inability to scale up its on-premise architecture on demand restricted the development of models in sequence, slowing time to market.
The Solution
Intesa Sanpaolo built its Democratic Data Lab on Google Cloud to address the challenges of its on-premise data analytics lab. This new environment allows the risk management team to develop and release risk management solutions more quickly, enabling better control and mitigation of risks. The Democratic Data Lab eliminates the separation between lab and production environments, allowing for seamless transition and faster release of solutions. By using BigQuery as its data warehouse, the lab can access the bank’s data without additional layers, streamlining the development process. The use of Google Kubernetes Engine allows the team to scale resources on demand, facilitating parallel model development and quicker solution releases. Managed services like Vertex AI and BigQuery free the team from infrastructure management, allowing them to focus on developing effective models. The Democratic Data Lab also democratizes access to risk management data insights across the bank, enabling business team members to navigate and query data independently. Looker dashboards provide real-time visualization of key risk indicators, aiding compliance with regulatory requirements and enhancing reporting efficiency.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
