Federal policy on improper payments spurs need for high performance analytics


Recently, top executives from SAS gathered in Washington, DC with customers and other interested parties to discuss the potential impact of "big data" and high-performance analytics on the U.S. government. Topics included cyber-attack strategies, health care, bio-surveillance, border security and of course, fraud and improper payments.

On the heels of the August 1, 2012 unanimous consent by the Senate to approve the Improper Payments Elimination and Recovery Improvement Act, SAS announced at the The Premier Business Leadership Series the new SAS DataFlux Event Stream Processing Engine.  While the bill makes its way to the House, I imagine government programs will continue to examine ways in which they can establish their own Do Not Pay initiatives given the resources they have in place.  For those programs where there are high volumes of data and the use of analytics that can tackle big data needs to be able to prioritize risk across relational and pattern-matching analysis in both structured and unstructured data environments, this new capability deserves a look.

Especially in light of the 2011 Government Accountability Office report estimating $115 billion of improper payment activity at 70 programs across 20 agencies, risk and fraud are issues we cannot ignore.  While part of the solution will be to coordinate data across agencies and match to known lists (i.e., deceased individuals or contractors in arrears on taxes), the ability to apply big data analytics that includes predictive modeling and near real-time analysis without an information lag should produce actionable intelligence that could automatically prevent a suspicious receiver of a payment from going out the door.   As the GAO mentions in their report, “Effective detection techniques to quickly identify and recover improper payments are also important to a successful reduction strategy.”

Does or will your reduction strategy include high-performance analytics that will need to be processed as data is streaming into your environment?  Who is making some headway in these areas?

Learn more about high-performance analytics in this special 32-page report on high-performance analytics.


About Author

John Stultz

Government Fraud Solutions Specialist, Security Intelligence, SAS

John Stultz is a Government Fraud Solutions Specialist for the Security Intelligence practice at SAS. His experience includes implementing architectures to deploy comprehensive data pre-processing, data validation and data management strategies that support the fusion of data and the ability to use advanced analytics to detect fraud. When he is not working with the Federal Government he can be found traipsing through the Blue Ridge Mountains with his wife and three boys.

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