Predictive Analytics: The Key to Combating Improper CARES Act Payments | MicroStrategy
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Predictive Analytics: The Key to Combating Improper CARES Act Payments

In late March, President Donald Trump signed the Coronavirus Aid, Relief, and Economic Security (CARES) Act, a $2.2 trillion stimulus package, into law. The act provides assistance, including cash payouts and loans, to businesses and individuals impacted by the COVID-19 pandemic. While the necessity of the package is undeniable, so now is the pressure on the government to quickly distribute this unique aid package with the required transparency and oversight.

Historically, improper payments (those that should not have been made or were made in incorrect amounts), have been a significant challenge for U.S. government agencies managing large distribution programs. According to the Government Accountability Office (GAO), in 2019 alone, the U.S. Government issued improper payments to the tune of $175 billion, with just over $75 billion deemed recoverable. Since 2003, the GAO estimates that improper payments have cost taxpayers approximately $1.7 trillion. 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.”

That’s where modern analytics can help. In order to effectively carry out the CARES Act and other aid programs, the U.S. Government should have complete visibility into its programmatic data—and 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—the kind that has been actively used in the private sector for many years, where the processing power of cloud computing and the ability to scan millions of data points (and develop machine-driven predictions) has informed and corrected the handling of large 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.), and this has been useful. Since the State of Maryland began using analytics to identify tax return fraud patterns, it’s now ten times more efficient than before at identifying fraud.

While identifying problem transactions is an important part of the battle against fraud and improper payments, the real goal is to proactively detect and correct potential problems before they occur. The few agencies that have already embraced advanced analytics are seeing real results, such as improved program management, enhanced service delivery, and optimized resource utilization, which 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 AI and ML, have been able to identify past fraud and improper payments—and also predict them. For example, the United States Postal Service Office of Inspector General used predictive analytics to identify high risk contracts, with 74% of those identified ultimately showing evidence of fraud.

It’s time for more government agencies to adopt and accelerate their deployment of advanced analytics. The investment would easily be offset by the savings realized through the reduction and elimination of improper payments in relief packages such as the $2.2 trillion CARES Act, where identifying just 0.05% of improper payments would save the government over a billion dollars in taxpayer funds—enough to cover CARES stimulus payments for the entire city of Seattle.

Want to learn about how analytics technologies like MicroStrategy are helping government agencies to save money and digitally transform? Watch this webcast.

This post was co-authored by Matt Ipri, MicroStrategy account executive and Rick “Ozzie” Nelson, senior vice president and general manager for the public sector at MicroStrategy.

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