Detection of fraud, waste, and abuse (FWA) is an important yet challenging problem. In this article, we describe a system to detect suspicious activities in large healthcare datasets. Each healthcare dataset is viewed as a heterogeneous network consisting of millions of patients, hundreds of thousands of doctors, tens of thousands of pharmacies, and other entities. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous network structure.

Healthcare-related programs include federal and series of technical challenges. From a data representation state government programs such as Medicaid, view, healthcare data sets are often large and Medicare Advantage (Part C), Medicare FFS, and diverse. It is common to see a state's Medicaid program Medicare Prescription Drug Benefit (Part D). Nonhealth-care or a private healthcare insurance program having programs include Earned Income Tax hundreds of millions of claims per year, involving Credit (EITC), Pell Grants, Public Housing/Rental millions of patients and hundreds of thousands of Assistance, Retirement, Survivors and Disability Insurance providers of various types, for example, physicians, (RSDI), School Lunch, Supplemental Nutrition pharmacies, clinics and hospitals, and laboratories. Assistance Program (SNAP), Supplemental Security Any fraud-detection system needs to be able to handle Income (SSI), Unemployment Insurance (UI), and the large data volume and data diversity. While healthcare data (insurance claims, health Data patterns from both sides are dynamic. The complexity records, clinical data, provider information, and others) of the problem calls for a rich set of techniques offers tantalizing opportunities, it also poses a to examine healthcare data. Healthcare financials are complex, involving a from a suspicious individual or activity (as singled multitude of providers (physicians, pharmacies, clinics out by the automated screening components) and and hospitals, and laboratories), payers (insurance interacts with the system to navigate through data plans), and patients. To design a good fraud-detection items and collect evidence to build an investigation system, one must have a deep understanding of the case. The two categories have quite different technical financial incentives of all parties. Starting from database indexing/caching for fast data retrieval and domain knowledge, auditors and investigators have user interface design for intuitive user-system interaction.