Detection of fraud, waste and abuse in healthcare systems is critical to minimise financial losses incurred by medical aid companies. Traditional fraud detection methods are based on manual processes and often fail to identify hidden patterns within medical claims data. The complexity and scale of fraudulent activities require more advanced, data-driven approaches. This study aims to develop a framework that employs unsupervised machine learning models to aid in and improve fraud detection from medical claims data for a South African medical scheme. The framework employs six unsupervised anomaly detection algorithms, including isolation forest (IF), one-class support vector machine (OC-SVM), autoencoder (AE), self-organising map (SOM), local outlier factor (LOF), and hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The first layer is focused on detecting global outliers, and the second layer is aimed at refining those classifications with locally outlier factors. The results show that the framework flags potential fraud, waste, and abuse and provides valuable insights into which medical practices should be investigated.