FNS officials in seven regional offices assist  headquarters officials in this oversight work. Another type of SNAP fraud is trafficking, in which benefits are  exchanged for cash or non-food goods and services. 2). Selected Households’ Out- of-State and In-State SNAP Purchases Had Similar Levels of Potential Trafficking

Based on our analysis of fiscal year 2017 transaction data in the three  selected states, we found that SNAP households without out-of-state  purchases were generally just as likely to have made the types of  purchases that may indicate trafficking of benefits as households with out- of-state purchases. Specifically, we found that for North Dakota and Washington, households  that made one or more purchases out of state had similar rates of  purchases flagged for potential trafficking compared to households that  shopped only in their home state. Selected States That Reported Conducting Additional Data Analytics Also Employed More Leading Practices and Cited Advantages in Using Data Analytics

Officials we interviewed in the five selected states that reported  conducting additional data analytics—the District of Columbia,  Massachusetts, Mississippi, Washington, and Wisconsin— employed  more of GAO’s leading practices for data analytics than the two states  that used data matching alone—New Mexico and North Dakota. Automating fraud detection. Officials in Mississippi reported that its overpayment  collections increased $2 million since moving to a new investigation  management system a few years ago that incorporates more data  analytics techniques. Obtaining organizational support. Officials in three states  said that a challenge to using more advanced data analytics was that  it could appear to profile recipients or make it appear to the general  public and to policy-makers that certain legitimate uses of SNAP  benefits, such as using benefits out-of-state, were not allowed. Officials in six selected states  cited the high costs and resource demands of using advanced data  analytics techniques. FNS Supported Certain States in Adopting Leading Practices for Data Analytics, but Assistance and Information Sharing Has Been Limited

		FNS Helped Some States Adopt Certain Leading Practices for Data Analytics

FNS provided individualized assistance and training to several states  across the country to build their capacity for data analytics on SNAP,  consistent with several of GAO’s leading practices. Conduct Data Mining

FNS has funded pilot projects, training, and grants to assist some states  in developing their capacity for data mining to identify potential fraud. According to officials, FNS is in the early stages of  promoting states’ use of data analytics for SNAP fraud prevention and  detection, and its efforts have focused on assessing the current capacity  of states to use data analytics and determining analytic practices that are  effective. Although FNS provided some training on using data analytics, it was not  conducted on a recurring basis, and state officials we interviewed  expressed concerns about their access to information on successful data  analytics approaches. Appendix I: Objectives, Scope, and Methodology

The objectives of this report were to review the following: (1) the extent to  which SNAP households in selected states are making out-of-state  purchases that may indicate potential recipient fraud; (2) the extent to  which selected states are using data analytics—including those applied to  out-of-state transactions—to find potential SNAP recipient fraud, and  what advantages and challenges, if any, have they experienced doing so,  and (3) how FNS has assisted states in implementing leading practices  for data analytics for fraud detection. To determine how selected state agencies are using data analytics to  identify potential SNAP recipient fraud, we interviewed officials from  seven state SNAP agencies about their efforts. Appendix II: Out-of-state SNAP Spending By State, Fiscal Year 2017

In fiscal year 2017, the share of SNAP benefits spent out of state varied  by state from approximately 1 percent to 13 percent, with most out-of- state purchases made in a border state.
Why GAO Did This Study

The federal government provided $64 billion in SNAP benefits in fiscal year 2017 to help approximately 42 million low-income individuals purchase food. SNAP is administered by FNS in partnership with states. To help reduce the risk of improper receipt or use of SNAP benefits, states use data analytics, including data matching and data mining, to identify patterns or trends indicative of potential fraud in SNAP purchases. Based on concerns about potential SNAP benefit trafficking across state lines, GAO was asked to review out-of-state transactions and states' efforts to combat such fraud.
This report examines (1) the extent to which SNAP households in selected states made out-of-state purchases that may indicate potential fraud, (2) the advantages and challenges selected states have experienced in using data analytics to identify potential fraud, and (3) how FNS has assisted states in implementing leading practices for data analytics. GAO analyzed fiscal year 2017 data on SNAP purchases for North Dakota, Washington, and the District of Columbia, which had large percentages of non-border out-of-state purchases and interviewed FNS officials and officials in these states as well as in Massachusetts, Mississippi, New Mexico, and Wisconsin about their use of data analytics compared with leading practices.

What GAO Found

Supplemental Nutrition Assistance Program (SNAP) recipients are allowed to spend their benefits outside their state of residence, and GAO's analysis of fiscal year 2017 SNAP data in three selected states found that overall about 2 percent of households made purchases, both in state and out-of-state, potentially indicative of trafficking—the prohibited exchange of benefits for cash or nonfood goods or services. Also, GAO found little difference in potential trafficking behaviors between households that made one or more purchases out-of-state and those that shopped only in their home state.
Officials in all seven states GAO reviewed said they conducted data matching. Officials in five of these states stated that they use more sophisticated data analytics including data mining to help identify potential fraud (see figure). These officials cited advantages to using more sophisticated analytics to automate fraud detection and prioritize cases, allowing them to focus investigative resources on cases most likely to involve fraud. For example, officials in Mississippi reported that overpayment collections increased $2 million since the state incorporated more data techniques into its fraud detection efforts. However, officials in all seven selected states cited factors such as high cost, resource demands, data limitations and organizational support as challenges that affect their ability to use or maintain more advanced data-analytics techniques.
The U. S. Department of Agriculture's Food and Nutrition Service (FNS) has helped some states adopt certain leading practices for data analytics, but its current outreach is limited. FNS has provided assistance to some states through pilot projects, grants, and training, but, beyond a recently issued guide, FNS has done little to disseminate information more broadly about successful efforts to adopt data analytics. FNS officials said they are in the early stages of promoting data analytics for SNAP fraud prevention and detection, and their efforts have focused on assessing the current capability of states to use data analytics and determining analytic practices that are effective. State officials GAO interviewed said that training provided was helpful but expressed concern about their access to information on successful data analytic approaches. Disseminating information to states on successful strategies could help states address challenges.

What GAO Recommends

GAO recommends that FNS more widely disseminate information to states about successful strategies used by states to adopt data analytics. FNS agreed with this recommendation.