Belfius utilizes Azure Machine Learning primarily for fraud detection and anti-money laundering. The platform allows them to calculate fraud risk scores quickly and efficiently, with plans to implement real-time scoring in the future. Additionally, they process hundreds of millions of transactions annually to identify potential money laundering activities, using machine learning models to generate risk scores for alerts.

Before adopting Azure Machine Learning, Belfius faced challenges such as a lack of overview of features, leading to data scientists rewriting the same code for different models. There was also no versioning control, which made coding time-consuming and hindered their ability to respond quickly to new opportunities.

The managed feature store increases agility in model building by allowing users to discover and reuse features, rather than starting from scratch each time. It supports consistent feature definitions across the organization, enhances the reliability of machine learning models, and reduces costs by managing materialization and monitoring automatically.