Spotting fraud with predictive analytics

One of the ripest opportunities for predictive analytics in the federal government is fraud and abuse.

June 9, 2020 4:05 pm

1 min read

This content is provided by Deloitte.

One of the ripest opportunities for predictive analytics in the federal government is fraud and abuse. The Government Accountability Office estimated that in 2019 alone, the federal government paid out $175 billion in improper payments. Of that, $75 billion was deemed to be recoverable. That’s money on the table for agencies willing to put in the work to pursue it, and some agencies have already started using predictive analytics to do so. In 2018, the most recent year for which data has been reported, the Department of Health and Human Services’ Office of Inspector General recovered $2.3 billion in healthcare fraud judgements and settlements. Predictive analytics have played a significant part in that, sniffing out likely cases of fraud for investigators to pursue. Coronavirus stimulus spending is only going to create more potential opportunities for fraud in government Tim Persons, the Government Accountability Office’s chief scientist, said these data analytics tools, powered by artificial intelligence algorithms, have helped agencies “deal with messier, larger data sets,” and serve as force multipliers for auditors and investigators to recover money from these improper payments.

Read more about how governments are using predictive analytics to help spot fraud in this Deloitte article.