Belfius uses Microsoft Azure Machine Learning to help detect fraud and money laundering
When Belfius, a prominent Belgian bank, started using AI and Machine Learning in operations, they struggled to synergize results for monitoring potential illegal activity. What did they do? Read this insightful customer story showing how by using Azure Machine Learning, Azure Synapse Analytics and Azure Databricks Belfius improved development time, increased efficiency and gained reliability.
How is Belfius using Azure Machine Learning?
Belfius utilizes Azure Machine Learning to enhance its capabilities in detecting fraud and money laundering. By implementing a managed feature store, the bank can operationalize machine learning features, allowing data scientists to focus on developing transformative features rather than managing data engineering tasks.
What challenges did Belfius face before adopting Azure Machine Learning?
Belfius data scientists were struggling with a lack of overview of features, leading to repetitive code writing for different data models. The absence of versioning control made coding time-consuming and hindered their ability to quickly seize new opportunities.
What are the expected outcomes of using Azure Machine Learning?
Belfius expects to improve efficiency and reliability in their fraud detection and anti-money laundering processes. With the ability to perform real-time scoring and reduce false positives, the bank aims to enhance customer personalization and meet stringent regulatory standards.
Belfius uses Microsoft Azure Machine Learning to help detect fraud and money laundering
published by UPCONNECT LABS LLP
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