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 to detect deceitful claims within minutes. Additionally, they process hundreds of millions of transactions annually to identify potential money laundering activities, using machine learning models to generate risk scores for suspicious transactions.

Before adopting Azure Machine Learning, Belfius faced challenges such as a lack of overview of features, which resulted in data scientists repeatedly rewriting the same code for different data models. There was no versioning control or search capability, making it difficult to act quickly and efficiently. This hindered their ability to maintain consistency across operational models and slowed down development time.

The Azure Machine Learning managed feature store enhances the work of data scientists at Belfius by allowing them to collaboratively develop and use features in production without the overhead of managing underlying pipelines. It increases agility by enabling the reuse of features, supports versioning, and encourages faster experimentation. This ultimately leads to more reliable machine learning models and reduces costs associated with feature development.