Big Data and Class Imbalance in Medicare Fraud Detection

Abstract

Traditionally, to detect Medicare fraud, a limited number of auditors, or investigators, are responsible for manually inspecting thousands of claims, but only have enough time to look for very specific patterns indicating suspicious behaviors. This chapter provides two case studies to demonstrate the effects of class imbalance with big data on the detection of fraud in the Medicare dataset with List of Excluded Individuals and Entities fraud labels. It utilizes three different datasets, with provider payment and utilization information, released by the Centers for Medicare and Medicaid Services: Medicare Provider Utilization and Payment Data: Physician and Other Supplier, Medicare Provider Utilization and Payment Data: Prescriber, and Medicare Provider Utilization and Payment Data: Referring Durable Medical Equipment, Prosthetics, Orthotics, and Supplies. The chapter considers these parts of Medicare because they cover a wide range of possible provider claims, the information is presented in similar formats, and they are publicly available.