Using Spark for Anomaly (Fraud) Detection

Anomaly detection is a method used to detect outliers in a dataset and take some action. Example use cases can be detection of fraud in financial transactions, monitoring machines in a large server network, or finding faulty products in manufacturing. This blog post explains the fundamentals of this Machine Learning algorithm and applies the logic on the Spark framework, in order to allow for large scale data processing. Indeed, this was a real SMS I received from my bank after trying to deposit some money to an online payment system I had never used before. If Spark is new to you, it is an top-level Apache project for large-scale data processing.