This new operation also involves ThirdLine, a supplier of risk assessment and auditing services to public agencies — and which last year “graduated” from a gov tech accelerator program operated by CivStart — along with UHY Advisors, a professional services firm.
The three organizations say they want to bring more “cutting-edge fraud detection and analytics tools” to those local government payments, according to a statement. The idea is to find patterns and other signs that might result from payment errors or outright fraud.
The statement touts the ability of this push to transform “financial oversight with advanced analytics in the DOGE era.”
As legal challenges and controversy build around the effort by Elon Musk and President Donald Trump to severely cut federal spending, that cost-cutting energy is filtering down into the gov tech space, along with state and local agencies. For instance, Florida recently announced the creation of its own Department of Governmental Efficiency (DOGE) task force, with artificial intelligence seen as a crucial part of that push.
“Governments are increasingly facing scrutiny over how they manage public funds,” said David Osborn, CEO and co-founder of ThirdLine, in the statement. “By partnering with Rutgers, we are delivering state-of-the-art solutions not only to detect and prevent fraud and waste but also help governments demonstrate their commitment to fiscal responsibility.”
Rutgers is no stranger to gov tech. The university has worked with the Government Finance Officers Association to “automate financial data extraction from local government reports” via the use of AI. This new program will advance such efforts.
“By applying continuous audit principles to vendor payment data, this partnership represents a natural evolution of our work with cities, counties, and school districts,” said Miklos A. Vasarhelyi, director of the Rutgers Continuous Auditing and Reporting Lab, in the statement. “Together, we’re tackling fraud and mistakes in local government payments with a level of precision and efficiency previously unattainable."
