Unemployment fraud meets analytics: Battle lines are clearly drawn

Across the globe, social benefits programs protect citizens in need. They’re especially important in times of crisis, like the COVID-19 pandemic. But fraudsters know that crisis increases the likelihood of mistakes and process gaps at agencies managing these programs. And many found their prey in the US during the pandemic. Citizens filing for unemployment insurance (UI) benefits bombarded agencies just as the CARES Act brought changes to UI systems. In turn, unemployment fraud quickly became the center of the battle between organized international criminal networks and government agencies.

Billions of dollars were lost to unemployment insurance fraud during the COVID-19 pandemic. These are just a few examples of US cases, some of which involved investigations by the Secret Service and the US attorney general:

• Scattered Canary, a ring out of Nigeria, caused severe damage in the state of Washington. Their UI fraud numbers grew to $576 million. This same ring also hit numerous other states.
• In Florida, hackers who hijacked computers using malware tried to file $500 million in claims in Maryland. 
• A state contractor hired to process claims for Michigan’s UI agency stole $2 million.

Types of unemployment fraud across the globe

Different countries structure unemployment benefits programs differently. In the US, it’s a shared responsibility between the federal government and states, with many requirements set by state law. Regardless of program structure, unemployment benefit fraud is a global issue according to the OECD. Many who commit fraud use similar schemes:

• People sometimes provide false, misleading or inaccurate information – often using personal information from stolen identities to access benefits.
• Some perpetrators use phishing scams to create fake websites so they can steal the data of companies eligible for emergency funds.
• Fraudsters establish fake companies to apply for benefits under government grants for businesses.

The far-reaching effects of unemployment fraud

Those collecting UI benefits based on false information create broad ramifications for many people and organizations. For starters, having high numbers of fraudulent claims undermines agencies’ efforts to maintain program integrity – slowing down processes and increasing workloads for already overburdened agencies. It increases the cost of managing programs, too. This cuts into funds that might otherwise be available to those who report unemployment.

Those committing UI fraud bring hardship to citizens legitimately reporting lost wages. These citizens must wait longer to receive benefits, plus they may need to report fraud if they’ve been the victim of a fraud scheme. And that’s not uncommon – unemployment fraud is associated with several fraudulent activities, including identity theft, money laundering and other types of fraud risk, like synthetic identity.

Identity theft and money laundering: Two big challenges

Two common companions to unemployment fraud are identity fraud and money laundering. In the case of the Nigerian fraud ring, for example, the attackers appeared to have extensive records of personally identifiable information (PII). They used these stolen identities to submit large numbers of claims to collect unemployment insurance. Next, they relied on “money mule” bank accounts belonging to other people – often unsuspecting – to transfer their UI insurance funds. This technique is a way to cover up the trail of illicit funds. It’s a clear case for why anti-money laundering is so crucial in battling financial crimes.

Analytics: Fighting back against unemployment fraud

In a situation that’s anything but business as usual, it’s crucial to act quickly. Analytics is a proven way to fight back. With analytics, you can identify and address the most high-impact threats first, then later shift your focus to lower-level risks.

To detect and prevent clusters of identity theft claims filed by criminal rings requires a different approach than what’s used to find issues with legitimate claimants. You’ll want to take a broader view in this situation. That means analyzing claims by the hundreds or thousands, identifying anomalies and patterns across groups of claims, then flagging issues to staff.

There are many data points you can analyze to find patterns of fraud. And there are known risks to watch for, such as patterns with out-of-state banks (like we saw with the money mule cases) and oddities in email patterns – like addresses that are shared across seemingly unrelated claimants. But it goes deeper than that.

Looking into patterns of employer wage filings or claimants’ work history can uncover deep discrepancies that clearly point to identity theft and unemployment fraud. Consider these examples:

• A restaurant may have shut down during COVID-19 and laid off its workers. But if that restaurant only employed 8 to 10 people over the last three years, why were 50 claims suddenly filed against that account?
• Why were claims filed against a construction company for workers whose recent histories showed that they were nurses or computer programmers?

Each of these examples reflect errors criminals make when using stolen identities. It happens because they have some of the details, but not all.

Analytics can stop unemployment fraud quickly. Using a cloud-based solution to reduce the load on core IT systems – and overburdened UI staff – is key. You can speed investigations and shut down payments more easily by raising the visibility of identity theft rings – so that all suspicious claims and risks are exposed collectively. Those force multipliers can help save billions from going into the wrong hands – and deliver benefits to those who truly need them. In turn, it could help prevent a wave of economic devastation.