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Fighting Fraud in U.S. Government Agencies through ID Verification, Analytics

Fraudsters have been running schemes on government programs essentially since those programs were first created. However, COVID-19 created an environment especially ripe for fraudulent activity. When the pandemic hit in early 2020, government unemployment offices were flooded with both legitimate requests as well as hits from scammers looking to take advantage of the system and the chaos caused by the flood of claims.

Access to new technology like bots and Artificial Intelligence has given criminals, both those acting individually and larger organized crime syndicates, the power to submit fraudulent benefit applications on a tremendous scale.

First, fraudsters either buy stolen IDs, many of which are purchased from the dark web or create synthetic IDs by combining various bits of identity data from different sources. Then, they employ bots to completely inundate government systems and slip in fraudulent applications, which often go unnoticed among the flood of legitimate ones.

As the government attempts to limit criminal activity, many agencies are working to deploy technology solutions that allow them to capture anomalies and detect fraud in programs like UI, Medicare/Medicaid and even the Supplemental Nutrition Assistance Program.

With nearly 30% of the fraudulent UI claims in larger states based on stolen Social Security numbers, it’s much more difficult for government agencies to catch anomalies. Implementing an automated identity verification (AIV) system can be a lifesaver for agency IT teams that are understaffed and overworked for several reasons:

  • Improved processing time – By automating ID verification, government agencies can quickly process more applications. Using real-time credit data can help eliminate fraudulent claims before they get into the system. Faster processing also contributes to a higher user satisfaction rate among legitimate applicants who experience a more efficient turnaround.
  • Reduced human error – AIV eliminates the potential for human error common when staff are feeling the stress of doing more with less. Even with well-trained and experienced employees in place, errors, omissions and misunderstandings can let fraudulent claims pass.
  • Less expensive than deploying new workers – The growing demand for qualified IT professionals makes these positions very competitive and often cost-prohibitive for agencies on a set annual budget.
  • Scalability – Even government IT shops that can find, hire and train qualified new employees must still deal with seasonal (end of quarter, end of year) or event-based (disaster, pandemic) scaling challenges that test their normal day-to-day workload. AIV can provide flexibility during times of peak demand.

A 2020 report commissioned by researchers at the Administrative Conference of the United States found that federal agencies were closing the gap and that 45% of the 142 agencies surveyed were also using AI and/or machine learning to assist in fraud analysis in two key areas:

  • Using data analytics to detect and diagnose fraud after the fact.

Data analytics can help supplement IT and financial auditing teams and improve the overall efficiency and effectiveness of their post-mortem audits. Analytics make it possible to quickly and efficiently compare the data from disparate systems, more confidently identifying anomalies between them.

  • Implementing behavioural analytics to prevent fraud.

As important as fraud detection, prosecution and recovery are, using behavioural analytics to help prevent fraudulent activity by verifying identity before a claim is ever paid out is the real opportunity.

As reported by OpenGov Asia, bipartisan members of the house recently introduced legislation that would require the government to drastically modernise the United States’ digital identity infrastructure. This bill establishes the Improving Digital Identity Task Force to establish a government-wide effort to develop secure methods for governmental agencies to validate identity attributes to protect the privacy and security of individuals and support reliable, interoperable digital identity verification in the public and private sectors.

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