Northrop team helps CMS detect high-risk claims fraud

Task order calls for developing a fraud prevention system

Northrop Grumman will lead a contractor team that will develop a fraud prevention system for the Centers for Medicare and Medicaid Services under a four-year task order worth $77 million.

The Predictive Modeling Development task order was awarded under the CMS Enterprise System Development indefinite-delivery, indefinite-quantity contract.

The task order calls for Northrop to help identify high-risk claims in support of the CMS National Fraud Prevention Program, whose mission is to prevent and detect fraud, waste, abuse and other improper payments under the Medicare program, according to a Northrop announcement dated June 30.

The goal of the National Fraud Prevention Program is to implement proven predictive modeling tools into the claims processing system to stop payment on high risk claims.

Predictive modeling is a combination of advanced analytic techniques, including predictive analytics, linkage analysis, outlier analysis, network analysis, behavioral analysis and other statistical techniques, the announcement states.

To achieve CMS' goal, the Northrop Grumman team will design, develop and implement a system that uses analytic approaches to generate alerts and evaluate results to identify and help prevent improper payments, fraud and abuse.

Northrop Grumman will implement predictive modeling and other analytic technology systems capable of reporting alerts based on risk scoring applied to near-real-time claims beginning July 1.

The company's teammates include National Government Services, of Indianapolis, and Federal Network Systems, a Verizon company in Herndon, Va.

Northrop Grumman Corp., of Los Angeles and Falls Church, Va., ranks No. 2 on Washington Technology’s 2011 Top 100 list of the largest federal government contractors.

About the Author

David Hubler is the former print managing editor for GCN and senior editor for Washington Technology. He is freelance writer living in Annandale, Va.

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