Using data analytics to prevent fraud related to coronavirus stimulus package

On March 27, 2020, President Donald Trump signed the $2.2 trillion coronavirus stimulus package into law, the largest economic stimulus package in United States history. The Coronavirus Aid, Relief, and Economic Security Act, known as the CARES Act, provides assistance, including cash payouts and loans, to businesses and individuals suffering from the pandemic. The necessity of the package is undeniable.  The government, with dozens of agencies involved in the effort, will be under immense strain to rapidly distribute aid while doing so with necessary transparency and oversight.

Historically, improper payments — payments that should not have been made or were made in incorrect amounts — is a significant challenge U.S. government agencies face in managing large distribution programs. According to the Government Accountability Office (GAO), in 2019 alone, the U.S. government issued over $175 billion in improper payments, an amount roughly equal to the combined annual budgets of the departments of Justice, Homeland Security and Transportation. Of this amount, over $75 billion was deemed recoverable, meaning the government could have recouped the money. In the wake of Hurricanes Katrina and Rita, the GAO estimated over $1 billion was lost to improper payments and fraudulent charges. Since 2003, the GAO has estimated that improper payments cost taxpayers approximately $1.7 trillion. But exact numbers are hard to find and the actual impact harder to estimate. According to the GAO, “the federal government’s ability to understand the full scope of its improper payments is hindered by incomplete, unreliable or understated agency estimates.” Solving the problem is immensely harder when the underlying facts aren’t readily available.

In order to effectively carry out the CARES Act and other pandemic aid programs, the U.S. government should have complete visibility into its programmatic data and be able to make decisions based on insights directly derived from it. In short, the government needs scalable, secure and powerful analytics technology to make sure resources aren’t lost to fraud, waste or abuse. Over the last two decades in the commercial sector, advanced analytics and business intelligence have been increasingly used for in-depth financial analysis and proactive planning. The processing power of cloud computing combined with the ability to scan millions of data points and develop machine-driven predictions have made these technologies essential to organizations and their handling of funds.

To date, the application of analytics at public agencies has focused primarily on traditional financial analysis — monthly reporting, regular budget analysis, voucher/payment analysis, etc. This level of analysis is certainly useful. Government transformation efforts that used analytics were twice as likely to succeed as those that did not. With traditional analytics, some measure of improper payment reduction is possible. Agencies could identify overpayments and transactions, including those that are sent to the wrong individuals or organizations or are erroneously paid multiple times. Since the State of Maryland began using analytics to identify tax return fraud patterns, it is now ten times more efficient than before at identifying fraud, recovering nearly $35 million annually.

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While identifying problem transactions is an important part of the battle against fraud and improper payments, the real goal is to detect potential problems in advance and analyze them before they actually occur. The public funds that can be saved by proactively reducing fraud and eliminating improper payments is massive. The few agencies that have embraced advanced analytics have identified real, measurable results, such as improving program management, enhancing service delivery, and optimizing resource utilization. All of these results have saved millions in improper funds allocation.

Not only that, regulatory authorities, grant managers, and oversight committees that have leveraged the power of data analytics, including artificial intelligence and machine learning, have been able to identify past fraud and improper payments, and more importantly, predict them. The United States Postal Service Office of Inspector General utilized predictive analytics to identify high risk contracts and 74% of those identified ultimately showed evidence of fraud.

More government agencies should move to adopt analytics and accelerate their deployment of advanced analytics. The investment in data analytics would be easily offset by the savings realized in the reduction and elimination of improper payments. Executing the CARES Act will bring this to light and agencies will be well-served to build the proactive analytics applications and systems that hone in on recovering funds and identifying and preventing improper payments from happening in the first place. If you consider the unprecedented Coronavirus stimulus package—approximately $2.2 trillion in stimulus payments headed to millions of Americans and businesses—identifying a mere 0.05% of improper payments would save the government over a billion dollars in taxpayer funds.

Rick “Ozzie” Nelson is senior vice president & general manager for the public sector at MicroStrategy. Matt Ipri is an account executive at MicroStrategy.