Abstract

PurposeDespite lessons learned from prior disaster relief funding programs, billions of dollars in fraudulent loans were issued by the Paycheck Protection Program (PPP) during the COVID-19 pandemic in the USA. The misuse of funds prevented business owners and their employees who are in true financial need from accessing program funds. The purpose of this paper is to identify techniques perpetrators used to obtain funds from the program illegally since its inception in March 2020 and concludes with suggestions on internal controls to reduce fraud occurrences in future relief packages.Design/methodology/approachThe authors analyze 106 loan fraud cases reported by the US Department of Justice and compiled by the Project on Government Oversight to examine methods individuals used to illegally obtain funds from the program. The authors complement the data with lender characteristics from Call Reports and Business Insights. They further compare the fraud sample to the entire population of PPP loans, which is available on the US Small Business Administration website. The authors report descriptive statistics, correlations and multivariate regressions.FindingsThe authors find that most fraud cases falsify tax data to access program loans and inflate payroll numbers to obtain larger loan amounts. Applicants who sought large amounts applied using multiple companies and across multiple lenders, consistent with the use of multiple loans to avoid the scrutiny of a single large loan with a single lender. The authors find that cases with larger amounts relied on less regulated lenders, such as lending companies, rather than more regulated lenders.Originality/valueThe PPP is part of the largest ever US stimulus in which the private sector allocated funds. This study provides novel evidence of how fraudsters adapted to the program's rules to defraud the government.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call