MAPFRE Accelerates Time to Business Value by 20% with AI
Customer Company Size
Large Corporate
Region
- Europe
Country
- Spain
Product
- DataRobot AI Platform
- AutoML
- MLOps
Tech Stack
- Amazon Web Services
- Microsoft Azure
- Amazon SageMaker
- Tableau
- Microsoft Power BI
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Digital Expertise
Technology Category
- Platform as a Service (PaaS) - Data Management Platforms
- Analytics & Modeling - Machine Learning
Applicable Industries
- Finance & Insurance
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Predictive Maintenance
- Demand Planning & Forecasting
- Fraud Detection
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
MAPFRE is the largest Spanish-owned insurer in the world, the largest multinational insurance company in Latin America, and one of the 15 largest European groups in terms of premium volume. The company operates in more than 100 countries across five continents, generating €27.3 billion annually. More than 34,000 employees of 80 nationalities serve 26 million people around the world. The company's business lines rely on advanced analytics to help make decisions on pricing, sales, retention, underwriting, and more.
The Challenge
MAPFRE, a Spanish insurance company, operates in over 100 countries, generating €27.3 billion annually. The company's analytics team is responsible for providing advanced analytics to help make decisions on pricing, sales, retention, underwriting, and more. However, given the demand for data insights, the team found it challenging to keep pace with the many incoming requests and deliver value quickly. The team needed to expedite its time to market in tackling new business challenges.
The Solution
To address this challenge, MAPFRE ESPAÑA chose the DataRobot AI Platform to automate analytics and expand productivity in meeting business needs. The company integrated DataRobot APIs with Amazon Web Services, Microsoft Azure, and Amazon SageMaker, and linked to the company’s data lake using an Athena driver. Models were then deployed to Tableau and Microsoft Power BI for easy use by line-of-business employees. The DataRobot platform eliminated the need to hand-code models, expediting the time to explore and find promising new use cases. MLOps simplified deployment and offered a single spot to monitor models in production.
Operational Impact
Quantitative Benefit
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