Fraud, Waste, and Abuse Detection in Medical Claims Using an Ensemble of Unsupervised Machine Learning Models

The identification 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 to detect. This paper develops a framework that employs unsupervised machine learning models to improve fraud detection from medical claims data for a South African medical scheme. The framework is a two-layer approach that employs six unsupervised anomaly detection algorithms, including isolation forest, one-class support vector machine, autoencoder, selforganising map, local outlier factor, and hierarchical densitybased spatial clustering of applications with noise. The first layer focuses on detecting global outliers, while the second layer refines these classifications using local 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 further. The paper highlights the potential of unsupervised learning for fraud detection in medical claims, reducing reliance on manual audits and improving detection accuracy.