Affiliations: Florida Atlantic University, Boca Raton, FL, USA
Correspondence:
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Corresponding author: Richard A. Bauder, Florida Atlantic University, Boca Raton, FL, USA. E-mail: rbauder2014@fau.edu.
Abstract: Access to copious amounts of information has reached unprecedented levels, and can generate very large data sources. These big data sources often contain a plethora of useful information but, in some cases, finding what is actually useful can be quite problematic. For binary classification problems, such as fraud detection, a major concern therein is one of class imbalance. This is when a dataset has more of one label versus another, such as a large number of non-fraud observations with comparatively few observations of fraud (which we consider the class of interest). Class rarity further delineates class imbalance with significantly smaller numbers in the class of interest. In this study, we assess the impacts of class rarity in big data, and apply data sampling to mitigate some of the performance degradation caused by rarity. Real-world Medicare claims datasets with known excluded providers are used as fraud labels for a fraud detection scenario, incorporating three machine learning models. We discuss the necessary data processing and engineering steps in order to understand, integrate, and use the Medicare data. From these already imbalanced datasets, we generate three additional datasets representing varying levels of class rarity. We show that, as expected, rarity significantly decreases model performance, but data sampling, specifically random undersampling, can help significantly with rare class detection in identifying Medicare claims fraud cases.
Keywords: Big data, Medicare fraud detection, class imbalance, data sampling, rare classes
