Opinion

Cutting Federal Workforce Not the Way to Eliminate Waste, Fraud and Abuse

Using data to proactively assess risks

Rep. Elijah Cummings, D-Md.; Sen. Thomas Carper, D-Del.; and Rep. Darrell Issa, R-Calif., discuss government entities and programs identified as "high risk" for waste, fraud, abuse or mismanagement. (Chris Maddaloni/CQ Roll Call file photo)

Presidential candidate Donald Trump campaigned on a pledge to “attack our debt and deficit by vigorously eliminating waste, fraud and abuse in the federal government.” Within weeks of the election, President-elect Trump announced plans to implement federal hiring freezes and curtail or eliminate protections that federal employees have had for more than a generation.

Waste, fraud and abuse in government are significant problems and President-elect Trump is right to focus on eliminating them. But cutting the federal workforce is not the path to doing so. A 1982 Government Accountability Office report found, for example, that the federal hiring freezes imposed under Presidents Carter and Reagan led to the loss of 445 IRS revenue agent and auditor staff, which resulted in government losses of tax revenue of more than 20 times the amount saved in salaries and benefits.

So how can President-elect Trump really make a dent in the fraud, waste, and abuse occurring within the federal government? He can strongly support the implementation of a new law aimed at curbing fraud in federal programs. The Fraud Reduction and Data Analytics Act, signed into law last June, calls for agencies to proactively assess their risks to fraud, evaluate the strength of their controls for identifying fraud, and strengthen those controls identified as weak or nonexistent to combat the biggest fraud risks. It also calls for agencies to use data analytics to fight fraud.

In June 2015, the GAO found that many federal agencies do not consider looking for potential fraud as part of their missions. Strapped for resources, these agencies instead prioritize getting benefits like disability, unemployment, and farm subsidy payments to beneficiaries. Often these efforts come at the expense of making sure that those who shouldn’t get benefits don’t get them.

If this comes as a surprise, it should. Many private sector companies have long focused as much attention on fraud prevention as they have on getting their products and services into the hands of their customers, for good reason. Fraud is big business.

Defrauding Uncle Sam has become a cottage industry. The federal government made an estimated $136.7 billion in improper payments — some of which was fraud — during fiscal year 2015, the largest total since agencies first began reporting data on the subject in 2004. Medicare and Medicaid account for the lion’s share of those payments.

In June 2016, the same month the Fraud Reduction and Data Analytics Act was signed into law, Health and Human Services Secretary Sylvia Mathews Burwell and Attorney General Loretta Lynch announced charges against 243 individuals, including 46 doctors, nurses and other licensed medical professionals, for their alleged participation in Medicare fraud schemes involving approximately $712 million in false billings. Just how much fraud occurs across the government is unknown because until the new law, agencies were not required to establish systems to proactively look for it.

[Opinion: Treasury Identifies Billions in Waste]

The new law forces government agencies to radically rethink their approaches to fighting fraud. Instead of mistakenly paying benefits out to fraudsters and then trying (often in vain) to chase them down, the law requires agencies to proactively assess their risks to help avoid fraudulent payments in the first place. Data analytics represent the best mechanism for federal agencies to root out fraud, as increasingly technology allows even non-tech-savvy program managers to find patterns that suggest fraud that they wouldn’t have otherwise identified. These tools are already regularly used to fight fraud in the private sector.

For example, in the commercial insurance industry, in addition to training their employees, implementing customer education programs and liaising with regulatory bodies, most insurance companies also use sophisticated software to identify the probability of fraud. By gathering and analyzing data from hundreds of thousands of claimants, they can detect the types of evidence that are more likely correlated with fraud as opposed to actual property damage. Data visualization tools also hold real promise for federal program managers, as they can chart anomalies on maps and graphs that allow for the swift identification of potential fraud.

President-elect Trump has an opportunity to make good on his campaign promise to reduce fraud, waste, and abuse in the federal government, but the way to do it has less to do with reducing the federal workforce, and more to do with empowering that workforce with the right tools to fight it.

Linda Miller is a director at Grant Thornton Public Sector, leading its fraud risk practice. Greg Wallig is a principal at Grant Thornton, consulting on advisory services.

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