Localities and States Are Turning to Data Analytics to Catch Fraudsters—and It’s Working

When a health care provider submitted a request for $8,002,021 to New York’s Medicaid program, it raised eyebrows among state auditors. Flagged as an abnormally large invoice, the state denied the payment and investigated the claim. It turned out that the vendor had inadvertently made a typo that combined the amount of the payment—$800—with the year—2021.

For New York and for an increasing number of government agencies, that vigilance has come in the form of data analytics, the process of using computers to collect huge stores of data and flag abnormalities, and then assigning humans to identify which transactions are legitimate and which are suspicious.

Over the past couple of decades, state and local governments have collected data to keep track of everything from tax returns to Medicaid, public assistance and unemployment insurance payments, pension checks, and employee hours worked. As more governments have converted to digital data collection systems, the need for and number of analysts to interpret that data has grown.

In Oregon, following an effort to parse the long list of deceased recipients on public assistance agents found more than 1,000 deceased recipients who had received a collective $6.8 million in payments, and 384 inmates on public assistance.

States and Localities ‘Primary Targets of Fraudsters’
Because different levels of some multitiered government assistance programs are administered by different agencies, fraudsters are finding ways to sneak past the gatekeepers. Couple that with the addition of benefits like telemedicine and changes in unemployment insurance rules during the pandemic, there are areas where no one person is responsible for monitoring all of the spending.

A potential solution is to collect more data; making online identity verification more personal; and borrowing data analytics practices from banks and credit card companies, which had a head start on governments when it comes to combatting the identity theft.

Monitoring Employees and Residents
Some county governments have turned to data analytics to monitor fraud and abuse by employees and residents. Governments can use data to spot abnormalities in the frequency with which employees access confidential records.

Increasingly, employees are accessing government data with the intention to sell it. The same is true of hackers posing online as legitimate contractors to request payments.

In Kansas, state auditors designed a program to spot potential fraud in employees’ use of state-issued credit cards, which workers swipe to pay for gas, travel expenses, and supplies.

In New York the state Comptroller’s Office used analytics to identify $100 million in improper payments to special education vendors who billed the state for personal expenses like furniture for their homes and gas for their personal cars.

While the use of electronic data analytics can save or recover millions of taxpayer dollars, it’s not cheap to get started. It is estimated that a basic fraud detection system could cost a few hundred thousand dollars, while a more advanced setup might top $1 million.

Original article here

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