Mastering Data Management

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How many improper payments are being sent out? How can you tell?

Improper payments from government agencies have been a long-standing and significant problem. While only a small percentage of overall government payments, they account for hundreds of billions of dollars in unnecessary expenses for government agencies at all levels.

Federal, state and local officials are faced with the challenge of identifying both the extent and root cause of improper payments while reducing risk and correcting problems. While much progress has been made in recent years, agency officials are looking at new solutions that will provide more timely and accurate identification of potential payment errors or fraud.

Most agencies today are already using sophisticated, statistics-based technology to assist them in identifying transactions that may represent improper payments. These detection applications look for anomalies in transactions or related sets of transactions (such as bills for incompatible treatments for the same individual).

If a transaction is identified as suspect, it is either denied or investigators are alerted to manually inspect it to determine whether a payment is improper.

These systems determine related sets of transactions using fixed identifiers such as Social Security number, drug enforcement administration number, taxpayer identification number, etc. Errors in these identifiers, either deliberate or unintentional, limit the ability of the detection applications to identify improper payments.

While these detection programs are asking the right questions, they don’t always have a complete and accurate set of data which they can be applied. Master data management (MDM) can help agencies solve this problem.

Instead of relying on one identifier, MDM solutions integrate disparate data simultaneously, providing a single, comprehensive view of all the different data associated with a particular person or other entity. The most accurate systems apply probabilistic matching techniques to the data, rather than less accurate rules-based or other traditional approaches.

So how does probabilistic matching work? We’ll cover that in our next post.