Medicare Fraud Detection Using Machine Learning Methods

Abstract

Healthcare is an integral component in people’s lives, especially for the rising elderly population, and must be affordable. Medicare is one such healthcare program. Claims fraud is a major contributor to increased healthcare costs, but its impact can be lessened through fraud detection. In this paper, we compare several machine learning methods to detect Medicare fraud. We perform a comparative study with supervised, unsupervised, and hybrid machine learning approaches using four performance metrics and class imbalance reduction via oversampling and an 80-20 undersampling method. We group the 2015 Medicare data into provider types, with fraud labels from the List of Excluded Individuals/Entities database. Our results show that the successful detection of fraudulent providers is possible, with the 80-20 sampling method demonstrating the best performance across the learners. Furthermore, supervised methods performed better than unsupervised or hybrid methods, but these results varied based on the class imbalance sampling technique and provider type.