There are two types of machine learning approaches that are commonly used in anti-fraud systems, unsupervised and supervised machine learning. They can be used independently or combined to build more sophisticated anomaly detection algorithms. Supervised learning involves training an algorithm using labeled historical data. In this case, existing datasets already have target variables marked, and the goal of training is to make the system predict these variables in future data. Unsupervised learning models process unlabelled data and classify it into different clusters detecting hidden relations between variables in data items.
To get more information on types of Internet Fraud and how to prevent them please read Part 1.