How to apply Unsupervised Anomaly Detection on bank transactions
A use case of Unsupervised Learning with Python, step-by-step
In this tutorial, we’ll show how to detect outliers or anomalies on unlabeled bank transactions with Python.
You’ll learn:
– How to identify rare events in an unlabeled dataset using machine learning algorithms: isolation forest (clustering).
– How to visualize the anomaly detection results.
– How to fight crime with anti-money laundering (AML) or fraud analytics in banks
Use case and tip from people with industry experience
