Data Mining for Compliance

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There are many categories of non-compliance and abuse problems associated with government-funded entitlement programs.  There is a wide variety of federal, state and local entitlement programs that are faced with this problem.  These include, programs such as TANF, Food Stamps, Medicaid, Nursing Home programs, Child Care programs. etc. 

Most states have special investigative staff that conducts investigations into the various administered public assistance programs.   Some common types of non-compliance include welfare recipients cashing aid and/or food stamps for which they are ineligible.  Government health insurance is another arena which has a vital need for compliance management. In the health sector, it is estimated that Medicare lost $11.9 billion to fraud and mistakes such as improper payments to doctors and hospitals in 2000 (U.S. Department of Health and Human Services 2001). Within the workers’ compensation system, broad categories of non-compliance include workers receiving improper benefits through intentional deception, health care providers and attorneys billing for services not rendered, misrepresentation of services, or employers avoiding payment of proper insurance premiums to gain a competitive advantage in the marketplace, etc.

Collectively, non-compliance within government entitlement programs leads to significant revenue loss at the local, state and federal level.  Current non-compliance monitoring is done based upon numerous sources of information such as anonymous referrals and computer cross matches. Compliance management that leverages data mining and other analytical techniques have a strong role to play in minimizing this revenue loss.