“The NSF Payments and Analytics Branch handles the daily operations of the agency’s grant-giving operations, such as processing grant payments and providing technical support to grant recipients for post-award financial processes. It also handles NSF’s obligations under the 2019 Payment Integrity Information Act, which sets requirements for agencies to cut down on improper payments.
More recently, however, branch chief Jesse Simons told Federal News Network that his office is experimenting in the ‘higher-growth’ field of payment analytics and compliance…”
“While much of this office’s analytics work remains in development, it gained attention in November, when the team behind NSF’s improper payments predictive model won the Association of Government Accountants’ 2020 Innovation Challenge…”
“NSF developed its predictive model in R programming language, and has made it available to other agencies on the open-source development platform GitHub.
‘When it comes to payment integrity, we all have a shared responsibility to monitor the problem. It’s just at different levels, based on what agency you’re at. So why not try to share, in crowdsourcing, the development of a common solution, because everyone’s required to comply to some extent? But each agency might have its own nuances and complexities, so that’s why I’m excited about getting the opportunity to share this with other agencies and see what other ideas they might be able to add to this,’ Simons said…” Read the full article here.
Source: NSF develops predictive model to flag improper payments in grants – By Jory Heckman, January 6, 2021. Federal News Network.