At Sanlam, South African Financial Institution, AI Helps Attract, Retain More Customers
Company Size
1,000+
Region
- Africa
Country
- South Africa
Product
- DataRobot AI Platform
- AutoML
- MLOps
Tech Stack
- AI
- Machine Learning
- Cloud Computing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Predictive Maintenance
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Sanlam is a purpose-led financial services group headquartered in South Africa, operating across a number of selected global markets. The company has been creating value for stakeholders since 1918—for more than 100 years—and is now the largest non-banking financial institution in Africa, in 33 African countries and 44 countries globally. Sanlam's mission is to empower generations to be financially secure, prosperous, and confident. The company uses data analytics to influence sales, improve client retention, help manage expenses, and support key strategic initiatives.
The Challenge
Sanlam, Africa’s largest non-banking financial institution, exists with the purpose of empowering generations to be financially secure, prosperous, and confident. However, the company was facing challenges with its data science operations. The open-source AI options they were using felt cumbersome to navigate and lacked critical explainability for business stakeholders and compliance. This was hindering their ability to drive critical business value levers such as sales and client retention. The company needed a more streamlined and transparent AI solution that could help them improve their operations and deliver better results.
The Solution
Sanlam decided to implement the DataRobot AI Platform, which offered end-to-end automation capabilities. This platform allowed them to expedite and expand their AI efforts for both data scientists and actuaries. Through a managed cloud environment, the company was able to use the platform’s best-in-class MLOps capabilities, including model performance monitoring. The platform also offered multiple AutoML deployment options, including a JavaScript embedding approach as well as an API integration between Sanlam and DataRobot. With MLOps, they could monitor models in production for data drift and could see the features driving each model much more easily. By understanding those underlying data points, Sanlam was able to deliver essential explainability to stakeholders.
Operational Impact
Quantitative Benefit
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