
Better use of data analytics in three pandemic relief programs could have prevented $79 billion in fraudulent payments through those programs, says a report that calls on agencies to apply lessons learned, including the need to take advantage of available information in pre-award vetting.
The Pandemic Response Accountability Committee attributed to stolen or invalid Social Security numbers that portion of the estimated $400 billion in fraudulent payments within the $5 trillion under the SBA’s COVID-19 Economic Injury Disaster Loan program and Paycheck Protection Program, and special unemployment insurance benefits administered by the Labor Department.
“When program guardrails were removed during the pandemic, a substantial amount of funds were rapidly disbursed without proper identity verification. Implementing pre-award verification helps streamline the vetting process before disbursal, preventing fraudulent payments from going out and ensuring that funds are disbursed with additional program integrity controls,” it said.
The $79 billion amount likely is low, it added, because in the PPP program the SBA did not require applicants to provide a date of birth; having that data point to cross-check against the Social Security numbers would have revealed still more cases of stolen or invalid numbers, it said.
The report said that the data analytics capabilities the PRAC has developed for detecting fraud should be applied before funds are disbursed in loan, and benefit programs during future emergencies-such as those due to natural disasters or financial crises–and in regularly appropriated programs.
Having access to SSA records is a key tool for pre-award identity verifications, it said; however, because the process to implement SSN verification agreements can be lengthy, it recommended that agencies act ahead of time “so that an information exchange can be set up as a part of program integrity controls.” The report also urged agencies to require the collection of date of birth information in benefit applications.
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