Four-step approach to government fraud detection


185207227_72dpiEvery day there are news stories of fraud perpetrated against federal government programs. Topping the list are Medicaid and Medicare schemes which costs taxpayers an estimated $100 billion a year. Fraud also is rampant in other important federal programs, including unemployment and disability benefits,  health care, food stamps, tax collection, Social Security and the list goes on an on.

Fraudsters have become more sophisticated, and the importance of using technology to empower anti-fraud controls has never been greater. Analytics can be used to identify anomalies and fraud patterns, as well as match and link the malignant social network of well-organized crime networks.

The primary goal is to detect fraudulent activity before it occurs. This requires government agencies to adopt a multifaceted, anti-fraud detection approach that combines sophisticated data integration with a hybrid analytical approach that focuses on these four steps:

  • Rules to filter activities and transactions that may indicate fraudulent behavior, based on specific patterns of activity.
  • Anomaly detection to detect individual and aggregated abnormal patterns and determine extreme outliers that warrant additional scrutiny.
  • Predictive models based on past fraud cases to identify similar characteristics within future claims; insights gained create formulas that score transactions for the probability of fraud.
  • Social network analysis to identify links between suspicious entities and individuals that would normally escape notice; by analyzing names, addresses, bank account numbers, dates of birth, SSNs and more, potential fraud rings can be identified that would otherwise appear to be disparate actors.

This SAS Fraud Framework is having a significant impact in the federal market. By integrating data from a wide variety of third-party data sources with federal agency data on a national level, criminal organizations are uncovered quickly, greatly limiting potential losses to the government and taxpayers. This approach can be tailored to meet the needs of different government agencies and can proactively address fraud in areas such as insurance, welfare and social services, Medicare and Medicaid, tax, unemployment and workers’ compensation and grants and purchasing.

One of the most exploited government programs is the federal food stamp program. Given the limited resources for fraud prevention, a huge black market has developed whereby recipients exchange their food stamp benefits for cash. Savvy criminal enterprises find that these exchanges can generate easy money with very little risk – and it’s gotten even easier with the government’s use of electronic benefits cards. The good news is that law enforcement and government agencies are successfully using multiple analytical methods to identify crime rings in their infancy and spare federal taxpayers billions of dollars in benefits payouts each year.

Federal agencies that oversee government benefits programs must make fraud a riskier undertaking by incorporating strategies and tools that put investigators on the offensive. Traditional methods and systems used by federal agencies aren’t enough to detect fraudulent activity in the early stages, before it occurs. New approaches must include analytic techniques that support prevention, and provide federal agencies a much-needed, and much more, robust fraud detection strategy.

You can learn more best practices for government fraud detection in this webinar.


About Author

Jon Lemon

Senior Industry Analyst, Federal Government

Jon Lemon has spent seven years at SAS helping federal, state and local government customers prevent fraud and manage performance. Prior to becoming a Solutions Specialist for SAS Federal, Lemon was a budget director at the Department of Homeland Security, where he formulated, executed and managed over $4.5 billion of appropriated funds annually. He also worked with Booz Allen Hamilton, where he supported performance management and fraud detection and prevention efforts throughout multiple federal civilian and defense agencies. He holds an MBA degree and a Finance Bachelor of Science degree.

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