The GAO concluded in a report issued in early-2021 that there are “advantages…in the use of data verification for both agencies and beneficiaries… For beneficiaries, agencies’ use of data to verify income or assets can reduce documentation beneficiaries must submit or help them receive benefits more quickly, according to GAO’s review of studies.” In short, the GAO concluded that, in many cases, federal agencies that oversee a number of income-dependent benefit programs, like energy assistance and housing assistance “may be missing opportunities to help state or local administering agencies enhance their data verification.” Data verification and “data analytic practices, can enable programs to identify potential fraud or improper payments…” Data verification and analytics are helpful to both the government and the people that receive public benefits.
For government agencies, data verification can help reduce improper payments and improve administrative efficiencies, particularly when data are timely and accurate. For public beneficiaries, agencies’ use of data to verify income or assets can reduce documentation beneficiaries must submit or help them receive benefits more quickly, according to GAO’s review of studies.
In 2021, the GAO conducted a review of several programs that assist low-income individuals and concluded, among other things, that better data matching can prevent fraud, waste, and abuse, and can better direct funds to people that really need it. In a report, the GAO looked at Earned Income Tax Credit (EITC), Housing Choice Vouchers, Low Income Home Energy Assistance Program (LIHEAP), Medicaid (Modified Adjusted Gross Income (MAGI) eligible), Supplemental Nutrition Assistance Program (SNAP), and Supplemental Security Income (SSI). All of these programs have income eligibility requirements.
The amount of improper payments made for a number of federal assistance programs is staggering. In 2019, improper payments for four programs was around $90b: Medicaid ($57.4b), the Earned Income Tax Credit (EITC) ($17.4b), Supplemental Security Income (SSI) ($5.5b), and SNAP ($4b).
For energy assistance programs, the GAO added that “[b]ased on [its] review of state plans, 13 agencies administering LIHEAP reported using no electronic data to verify beneficiaries’ income, verifying income in other ways, such as checking beneficiaries’ documents.” The report added that while the U.S. “Department of Health and Human Services (HHS) has encouraged LIHEAP agencies to use electronic data to improve program integrity, [it] has not taken recent steps to share information that could facilitate its use. HHS officials said that doing so could help state agencies’ verification efforts.
For housing assistance programs, the GAO noted that while “state [and] local public housing agencies administering Housing Choice Vouchers have the flexibility to use other data sources [to verify benefits information…the [U.S.] Department of Housing and Urban Development (HUD) has not made efforts to better understand or share information on the use of other data sources that could further enhance efficiency or accuracy in verifying beneficiary income.” This lack of verification is unfortunate because a HUD official who oversees the housing choice voucher system said that “[a]dditional efforts could help housing agencies learn about ways to enhance their current data verification practices…”
The GAO noted, in a report on how federal agencies can reduce fraud, noted that
[d]ata analytics includes a variety of techniques to prevent and detect fraud, including data matching and data mining. According to GAO’s Fraud Risk Framework, data matching can help prevent and mitigate the risk of fraud occurring, uncover potential fraud once it has already occurred, after payments have been made, and assist programs in recovering these dollars. In addition to verifying initial eligibility, data matching can enable programs that provide ongoing benefits to identify changes in key information that could affect continued eligibility.
We previously reported that the use of data analytics can help low-income programs identify potential fraud. For example, we reported that data analytics can help state agencies administering the Supplemental Nutrition Assistance Program (SNAP) identify meaningful patterns in data to determine potential cases for further review. State SNAP agencies reported advantages to the use of data analytics in their anti-fraud efforts, including automating fraud detection, financial savings, prioritizing and enhancing fraud investigations, and preventing fraud.
U.S. Gov’t Accountability Off., GAO-21-183, Federal Low-Income Programs: Use of Data to Verify Eligibility Varies Among Selected Programs and Opportunities Exist to Promote Additional Use, 14 (Feb. 2021).