Input:
	Background

		Federal and State Roles in Addressing SNAP Fraud

The goal of SNAP, formerly known as the federal Food Stamp Program,  is to help low-income individuals and households obtain a more nutritious  diet by supplementing their income with benefits to purchase allowed food  items. The federal government pays the full cost of the benefits and  shares the responsibility and costs of administering the program with the  states. The overarching rules governing SNAP are set at the federal level.  Accordingly, FNS is responsible for promulgating program regulations  and ensuring that state officials administer the program in compliance  with program rules. FNS officials in seven regional offices assist  headquarters officials in this oversight work. FNS also determines which  retailers are eligible to accept SNAP benefits for food purchases and  investigates and resolves cases of retailer fraud. The states, or in some  cases counties, administer the program by determining whether  households meet the program’s eligibility requirements, calculating  monthly benefits for qualified households, and issuing benefits to  participants on an electronic benefit transfer (EBT) card. States are also  responsible for investigating possible violations by benefit recipients and  pursuing and acting on those violations that are deemed intentional.

		Types of SNAP Fraud and State Anti-Fraud Mitigation Strategies

Intentional program violations include acts of fraud, which involve  obtaining something of value through willful misrepresentation. Eligibility  fraud involves individuals making false or misleading statements in order  to obtain benefits, including statements about household composition,  household expenses, and income. Failing to report changes to household  circumstances that may affect benefits can also result in eligibility fraud  under certain circumstances. When recipients are certified for SNAP,  state agencies assign them to a reporting system for notifying the state of  certain changes.  These changes include when they have a change of  address, both in-state or out-of-state. Some systems require recipients to  report within a certain period of time of the change occurring, often within  10 days.  Other reporting systems– including simplified reporting – require  recipients to submit reports periodically.  Households subject to reporting  on a periodic basis must generally submit reports not less often than once  every 6 months.   One type of eligibility fraud is dual participation, in  which a recipient receives benefits in more than one state in the same  month.
Another type of SNAP fraud is trafficking, in which benefits are  exchanged for cash or non-food goods and services. Trafficking may  occur when recipients collaborate with retailers who pay cash for SNAP  benefits. For example, a retailer might allow a recipient to charge $100 on  his or her EBT card and then pay the recipient $50 instead of providing  food. Trafficking also occurs when a recipient exchanges an EBT card  and the corresponding Personal Identification Number (PIN) for cash or  non-food goods or services (e.g., rent or transportation) from another  individual.
According to a September 2012 USDA Office of Inspector General (OIG)  report, the magnitude of program abuse due to recipient fraud is unknown  because states do not have uniform ways of compiling such data. OIG  recommended that FNS determine the feasibility of creating a uniform  methodology for states to calculate their recipient fraud rate. In 2014,  FNS responded that it would be infeasible to implement the  recommendation as it would require legislative authority mandating  significant state investment of time and resources in investigating,  prosecuting, and reporting fraud beyond current requirements.
States must adhere to various federal requirements for detecting SNAP  recipient fraud, conducting investigations, and providing due process prior  to disqualifying recipients from participating in the program. The  household is responsible for repaying ill-gotten or misused benefits.  States may generally retain 35 percent of the fraudulent benefits they  recover, and the rest are returned to the federal government.

		Data Analytics

The use of data analytics enables the discovery and communication of  meaningful patterns in data so that states can determine which potential  SNAP fraud cases to review in detail. States have access to various types  of data in their case management systems, including recipient-provided  information and benefits data collected throughout the SNAP eligibility  determination process. Other information sources available to states  include transaction data collected by EBT processors, data from previous  fraud investigations, and third-party data from other government agencies  or commercial vendors (see fig. 1).
Data-analytics activities can include a variety of techniques to prevent  and detect fraud, including data matching and data mining. Data matching  is the large scale comparison of records and files to detect errors or  incorrect information. It can be used to verify information provided by  recipients or detect unreported changes. Data mining is the use of  automated computer algorithms to detect otherwise hidden patterns,  correlations, or anomalies within large data sets indicative of potential  fraud, thus assisting programs in recovering these dollars (see fig. 2).
Federal laws and regulations require states to conduct certain data  matches when an application for benefits is submitted and other times to  verify an individual’s reported employment and immigration status, as well  as to ensure the information provided is not for an individual who is  incarcerated, deceased, or disqualified from the program (see table 1).
GAO’s Fraud Risk Framework identifies the following leading practices to  help managers effectively use data to mitigate the likelihood and impact  of fraud (see table 2).
While these leading practices can help managers design and implement  effective data-analytic tools and techniques to prevent and detect  potential fraud, as discussed in the Fraud Risk Framework, these  techniques alone may not be sufficient to ensure that ineligible individuals  do not fraudulently enroll in a program or receive benefits. As a result,  managers may need to combine data-analytics activities with additional  controls as part of their efforts to combat fraud, in a strategic, risk-based  manner.

	SNAP Transaction Data from Selected States Show Relatively Few Households with Out- of-State Purchases Indicating Potential Fraud

		Out-of-State Purchases Are Allowed by SNAP Rules and Their Dollar Value Represents a Small Percentage of Purchases

A relatively large number of SNAP households made purchases outside  their home state, as allowed under the SNAP statute, but the total dollar  value of out-of-state purchases was small compared to SNAP purchases  overall, according to our analysis of FNS SNAP transaction data. We  identified approximately 5.5 million households that made out-of-state  SNAP purchases in fiscal year 2017. In comparison, FNS reported that  the monthly average number of SNAP households was approximately 21  million in fiscal year 2017. Out-of-state purchases made up approximately  3 percent of all SNAP benefits in fiscal year 2017, with a total dollar value  of about $2 billion (see fig. 3).
Out-of-state purchases may occur for different reasons, one of which may  be because a recipient lives on or near a state border, and regularly  shops across the state line. For example, District of Columbia recipients  spent about half of their SNAP benefits out of state in fiscal year 2017. All  District of Columbia residents are in close proximity to both Maryland and  Virginia, which are no more than approximately 7 miles from any point in  the District. In general, about a third (34 percent) of households  nationwide with out-of-state purchases spent $50 or less on those  purchases in fiscal year 2017. See Appendix II for a detailed listing of out- of-state purchases by state. Out-of-state purchases may also indicate  potential program violations, including eligibility fraud or trafficking.  However, because out-of-state purchases are permitted, analysis of  additional household and transaction information is generally needed to  identify potential fraud, as discussed below.
Of out-of-state transactions, purchases in a state that did not border the  recipient’s home state (non-border state) made up approximately 1  percent of all SNAP benefits in fiscal year 2017, as shown in figure 3  above. There were 2.2 million SNAP households that made at least one  purchase in a non-border state in fiscal year 2017, and the percent of  SNAP benefits spent in a non-border state in that year ranged between  approximately 0.6 percent and 1.9 percent. In fiscal year 2017, states  whose SNAP recipients spent the highest percentage of their SNAP  benefits in non-border states included Colorado, Hawaii, Montana, North  Dakota, and Rhode Island.

		SNAP Purchases in Non- Border States Raise Questions of Residency for a Relatively Small Percentage of Households in Selected States

Overall, we found that for fiscal year 2017, less than 0.5 percent of  households in our three selected states spent all their SNAP benefits for  the entire fiscal year in a non-border state (see table 3). Use of benefits  in stores that are a long distance from a recipient’s residence for  extended periods of time, such as purchases exclusively in non-border  states over multiple months, could be an indicator of program violations,  including eligibility fraud. The total value of SNAP transactions by  households in our three selected states that made all purchases in non- border states in fiscal year 2017 was approximately $1.9 million. These  purchases represent about 0.1 percent of all SNAP benefits for fiscal year  2017 in the three selected states.
When SNAP benefits are used in a non-border state over an extended  period of time, this could indicate possible intentional program violations  such as an unreported move and other household changes that could  impact eligibility. SNAP officials we interviewed said that in some cases a  recipient may delay reporting a move if they are enrolled in SNAP in a  state with a lower barrier to entry to the program. At the same time, the  rules around reporting a move and residency may make it difficult to  determine when a recipient has violated program rules. Recipients are not  required to immediately report a move in some cases due to simplified  reporting rules that allow a recipient to report household changes only  periodically, generally every 6 months. Also, officials we interviewed in  the three selected states told us that there are no set time limits for a  SNAP recipient to reside in a new state before the former state revokes  the recipient’s residency. For example, a recipient may be out of state for  an extended period of time for personal reasons, such as helping a  relative, but still intend to reside in the state where they are enrolled in  SNAP. In that case, according to state officials, the recipient would not  necessarily need to report a move and may not be violating program  rules.
In addition to the program violations related to an unreported move, use  of SNAP benefits in a non-border state over extended periods of time  could bring into question whether a recipient is also enrolled in SNAP in  another state (i.e., dual participation). Also, it may indicate changes in the  household that could impact eligibility, including questions about whether  a recipient is earning unreported income in the state where they are using  their benefits. While state SNAP agencies stated that they conduct data  matching meant to detect dual participation and unreported income,  states also noted challenges with these matches. State agencies told us  that they use the PARIS system to detect possible dual participation, and  both NDNH and the Work Number to identify recipient income.  However, challenges officials cited in using these systems included lags  in the data provided, and additional work required to confirm data. The  use of data analytics to review recipient transaction data may help states  identify suspicious household activity more easily than with data matching  alone given the challenges associated with these systems. In addition,  data analytics may be another tool to help states identify suspicious  activities in a timely manner. Given the possibility for eligibility fraud or  other program violations, we plan to refer the households that our data  analysis identified as spending all benefits in a non-border state to their  respective state SNAP agencies for further investigation.

		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. Overall, we found that approximately 2 percent of all  households in the three selected states, including both households that  shopped out-of-state and those that shopped in state only, had a high  number of purchases potentially indicative of SNAP trafficking. However,  for two selected states, there was little to no difference in the percentage  of households with this activity when we compared households that only  shopped in their home state and households that shopped out-of-state.  For one state, a greater percentage of households that shopped out-of- state had purchases indicative of SNAP trafficking, but households in this  state also had different shopping patterns in general, as discussed below.  In addition, for households that shopped out-of-state, few of the  transactions we flagged as indicators of potential trafficking occurred  outside the home state. Although we found that rates of trafficking  indicators were generally similar between households that shopped out- of-state and those that only shopped in their state of residence, the  analysis of transaction data for other factors may allow states to identify  households at risk of trafficking and make them a higher priority for  investigation. Our prior work reported on the benefits of SNAP transaction  data analysis for this purpose.
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. This held true both for households that  only shopped in border states, as well as for households that shopped in  non-border states (see table 4). For example, 1.4 percent of Washington  SNAP households that only shopped in their home state had purchases  resulting in 20 or more trafficking flags in fiscal year 2017, and 1.8  percent of Washington households that also shopped in border states had  20 or more trafficking flags. For Washington households that also  shopped in non-border states, 1.5 percent made purchases resulting in  20 or more flags.
Our analysis of District of Columbia households identified higher rates of  potential trafficking indicators for households that shopped out-of-state,  compared to the other two selected states. Specifically, 1.4 percent of  District of Columbia SNAP households that only shopped in their home  state had purchases resulting in 20 or more trafficking flags in fiscal year  2017, and 5.7 percent of households that also shopped in border states  had 20 or more trafficking flags. For District of Columbia households that  also shopped in non-border states, 8 percent made purchases resulting in  20 or more flags. However, the difference in rates for District of Columbia  trafficking indicators may reflect the different shopping patterns of its  households when compared to other states. As stated previously, District  of Columbia households made about half of their SNAP purchases out-of- state, which is a significantly higher amount compared to any other state.  And all District of Columbia households are in close proximity to the  bordering states of Maryland and Virginia, approximately 7 miles or less.  Also, a small percentage of District of Columbia households shopped only  in their home state in fiscal year 2017—approximately 7 percent of all  households reviewed. In comparison, approximately 62 percent of North  Dakota households, and 76 percent of Washington households made all  purchases in their home state.
For the households in North Dakota and Washington that shopped out-of- state in fiscal year 2017, we found that most transactions indicating  potential trafficking occurred in the recipient’s home state rather than out- of-state (see fig. 4). District of Columbia households were the exception  and most transactions indicating potential trafficking occurred in the  recipient’s home state or in a border state. However, the pattern of  trafficking flags also aligns with where District of Columbia SNAP  recipients tend to shop, given that approximately half of their SNAP  purchases were made in border states in fiscal year 2017.
While we identified households in selected states with out-of-state  purchases that indicated potential trafficking, identifying such households  required additional data analysis of factors beyond purchase location.  Analysis of additional data elements may allow states to better identify  potential trafficking requiring investigation. We found out-of-state  purchase information alone is of limited benefit to identify SNAP  households that may be engaged in trafficking.

	Some Selected States Reported Using Data Analytics Beyond Required Data Matching and Cited Advantages As Well As Organizational and Resource Challenges

		Selected States Reported Doing Required Data Matching, and Some Reported Conducting Additional Data Analytics

Officials we interviewed in all seven of the states we selected for review  of use of data analytics reported conducting federally required data  matching to verify information provided by households when they initially  apply or recertify for SNAP benefits. Federal law and regulations require  states to conduct certain data matches when determining SNAP eligibility,  including matches that provide information on people who may be  incarcerated, deceased, or disqualified from receiving SNAP benefits due  to intentional program violations. The five databases that state SNAP  agencies are required to conduct matches against when determining  SNAP eligibility are the Department of Health and Human Services’  (HHS) National Directory of New Hires, the Social Security  Administration’s (SSA) Prisoner Verification System, SSA’s Death Master  File, U.S. Citizenship and Immigration Services’ Systematic Alien  Verification for Entitlements and FNS’s Electronic Disqualified Recipient  System (eDRS). As we previously reported, state SNAP agencies use  data matching to obtain information about households’ income, verify  information provided by households, or identify potential discrepancies.  Specifically, agencies are required to verify household data electronically  by matching their data with specific government sources and have the  option to match against additional data sources.
In addition to the required data matching, officials we interviewed in all  seven selected states also reported conducting other data matching with  a range of internal and external data sources. These matches used  information from federal, state, and commercial data sources on earned  income from employment or self-employment or unearned income from  other government benefit programs. According to state officials, these  sources included Unemployment Insurance information from state  workforce agencies, the PARIS file from HHS, and The Work Number, a  commercial verification service. Other sources that could be used include  Old-Age, Survivors, and Disability Insurance income information and  Supplemental Security Income information from multiple data matches  with the SSA. In addition to verifying applicants’ initial eligibility, data  matching can identify changes in key information that could affect  continued eligibility.
Beyond data matching, officials in all seven selected states said that they  had access to EBT reports notifying them of suspicious transactions,  although the type and frequency of use of these reports varied. For  example, while some state officials said that they manually generated  reports on an ad hoc basis, other state officials said that they had  automated reports that they received and reviewed on a weekly or  monthly basis. As we previously reported, automating data analytics tests  can allow agencies to monitor large amounts of data more efficiently than  with manual tests. Furthermore, officials in all seven selected states  reported that they had examined out-of-state transactions to some extent.  Some states had access to out-of-state reports as part of their suite of  EBT reports but did not review them often, while other states  automatically received alerts if households consistently used benefits out  of state over a certain extended period of time, such as 70 or 90 days.  For example, officials from Massachusetts told us that they flag certain  transactions to help ensure recipients comply with the state’s residency  requirements for eligibility. Specifically, after a client spends their benefits  out of state for 70 days or more, the state agency will send a letter asking  the client to prove they are still a Massachusetts resident. Officials  generally reported that tracking out-of-state transactions was most useful  for finding potential dual participation—a household receiving benefits in  two or more states.
Officials we interviewed in five of seven selected states reported  conducting further, more sophisticated data analytics involving data  mining—the active and recurring monitoring of EBT transactions using  algorithms to detect and flag transactions that indicate potential recipient  fraud, often on a real-time or near real-time basis. For example, officials  told us that these states—the District of Columbia, Massachusetts,  Mississippi, Washington, and Wisconsin—examined a range of indicators  of potential recipient fraud. Some of the five selected states automated  their data mining to monitor data for potential fraud indicators on a  continuous, real-time basis.
In addition to data mining, some of these five states reported using other  more advanced data analytics techniques, including mapping analysis  and a form of predictive analysis to identify SNAP purchases that could  indicate trafficking. For example, officials in the District of Columbia  reported using location mapping to identify households that spent their  benefits long distances from home. Officials we interviewed in Wisconsin  reported developing an automated check intended to flag particular types  of case characteristics indicative of potential fraud. According to the  Wisconsin officials, if a particular case is flagged, a caseworker must  follow up and provide extra scrutiny before the case can move forward in  the eligibility process. As we previously reported, certain types of  predictive data analytics can increase the effectiveness of anti-fraud  programs by identifying particular types of potentially fraudulent  behavior.

		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.
Organizational and leadership support. The five states with more  sophisticated data analytics techniques all reported to us that they  had organizational and leadership support for those activities. GAO’s  leading practices state that to be effective, data-analytics initiatives  need support across the program and, in particular, from program  managers. Officials in these states cited support from executive and  legislative state leadership for the use of data analytics to combat  SNAP recipient fraud. For example, officials in Wisconsin reported  that the governor’s office worked to centralize the agency’s data- analytics activities and support infrastructure to improve business  processes. Officials in Mississippi told us that the state’s executive  leadership fully supports the use of data to combat SNAP recipient  fraud and that the state legislature in 2017 passed a law to assist in  the identification of waste, fraud, and abuse.
Pursue external data. These states also reported to us that they  were able to obtain external data necessary for their data analytics  activities. For example, officials in Mississippi told us that they  interface with an array of data sources, including the National  Accuracy Clearinghouse, the state Department of Employment  Security, and the state Department of Education, among others.  GAO’s leading practices state that using data from other federal  agencies or third-party sources can help managers identify potential  instances of fraud. As we mentioned previously, the states that  reported conducting additional matching beyond that required by  federal law and regulation also reported using an array of federal,  state, and third-party sources for these data matches.
Consider program rules or previously encountered schemes.
These five states also reported that they considered program rules  and known or previously encountered fraud schemes to help design  their data analytics practices, another of GAO’s leading practices for  data analytics. These leading practices note that by using information  on previously encountered fraud schemes or known fraud risks,  managers can identify signs of fraud (i.e., red flags) that may exist  within their data. For example, two states reported that they change  their data analytics techniques in response to changing patterns of  fraud.
All five selected states that reported conducting additional data analytics  practices beyond data matching cited a number of associated  advantages, including increased efficiency and effectiveness of their anti- fraud efforts.
Automating fraud detection. All five states reported that data  analytics provided the advantage of automating the detection of  potentially fraudulent activity. For example, officials in Mississippi  noted that a new investigation management system implemented in  their state would use algorithms to detect potential fraud and  automatically generate flags, whereas in the past they had to examine  transactions manually.
Financial savings. Four states reported that data analytics had the  advantage of financial savings through the collection of overpayments  and the closure of cases. For example, officials in Washington said  that its data matching activities saved millions of dollars through the  closure of cases. 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.
Prioritizing and enhancing investigations. Four states reported that  data analytics helped them prioritize and enhance fraud  investigations. For example, officials in Washington said that they had  a system in place that used an algorithm to rank each fraud referral  based on a number of factors and moved higher-risk referrals to the  top of the list of investigations. Officials in Wisconsin said that they  combined eligibility, transaction, and retailer data and analyzed it to  produce a prioritized list of individuals who appeared most likely to  have trafficked at a specific retailer, allowing them to focus their  investigative resources on cases most likely to be fraud.
Preventing fraud. Finally, two states reported that data analytics had  the advantage of improving the return on investment of anti-fraud  activities through the prevention of fraud before it occurs. For  example, officials in Wisconsin estimated that data analytics has  helped them prevent a large proportion of fraud before it occurs,  thereby improving the cost-benefit of their anti-fraud practices.  Officials in Mississippi noted that data analytics can be an effective  deterrent.

		Selected States Reported Organizational and Resource Challenges in Effectively Using Data Analytics

Officials we interviewed in all seven selected states reported a range of  organizational and resource challenges that either prevented them from  using more advanced data analytics techniques or made their current  data analytics practices difficult to implement.
Quantifying benefits of data analytics. Officials we interviewed in  two states said it was challenging to quantify the benefits of data  analytics, therefore resulting in a lack of sound evidence for  supporting the utility of this type of work. For example, officials in  Washington reported that it was difficult to conduct a cost-benefit  analysis of data analytics because of the challenge of quantifying how  often fraud is prevented before it occurs. Officials in Wisconsin  reported that it attempted to measure future savings from fraud  prevention but that there is no guidance for how to determine these  savings.
Obtaining organizational support. Officials in two states reported  that it was challenging to obtain sufficient organizational support for  conducting data analytics. For example, officials in North Dakota  reported that they could not say how much support exists in the state  government to pursue additional resources for data analytics. Those  in the District of Columbia noted that it is sometimes difficult to  convince certain employees of the need for data analytics to detect  fraud.
Appearing to criminalize legitimate use. 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. For  example, Washington tracked the number of replacement EBT cards  as a possible indicator of fraud, but officials said that there were many  cases in which the client had legitimate reasons for needing a high  number of replacement cards, such as mental health issues or  homelessness. Washington officials further noted the challenge of  using demographic data in a predictive model, reporting that it puts  them at risk of profiling even though it can be helpful. For example,  when they examined recipients with high balances on their EBT cards,  demographic information provided an explanation. In particular,  elderly individuals were being frugal with their benefits.
Dealing with changing patterns of fraud. Officials we interviewed in  three states said that a challenge to using data analytics was dealing  with changing patterns of fraud. They said that the characteristics of  transactions that may indicate potential fraud are constantly changing  as fraudulent actors change their tactics in response to state  enforcement. For example, officials in Mississippi said that recipients  committing fraud might change from high-dollar to low-dollar  transactions, in which case the state would need to adjust its  monitoring accordingly.
Obtaining necessary data. Officials we interviewed also reported  challenges with obtaining data needed to conduct data analytics.  Officials in three states said that simplified reporting presents a  challenge to using data analytics to detect potential recipient fraud.  Specifically, simplified reporting made it challenging to use certain  information as potentially indicative of fraud because recipients are  not required to report certain changes—for example, a move out of  state—until it is time for them to recertify for benefits. In addition,  officials in three states reported a challenge in verifying necessary  data in order for them to be considered reliable for use. For example,  Massachusetts reported that one of the biggest challenges of  developing investigative leads through data analytics is that not all  data are considered equally reliable. For SNAP, FNS guidance  defines some data matches as “verified upon receipt” if the match is  with a primary or original source of the data (such as information on a  government benefit provided by the administering agency, such as  SSA). Eligibility workers can use this information without taking  additional steps to verify that the data are accurate, according to FNS  guidance. In contrast, data from a secondary source, defined in the  guidance as not being verified upon receipt, require additional  verification before the state agency can take action on an eligibility  determination.
High costs and resource demands. Officials in six selected states  cited the high costs and resource demands of using advanced data  analytics techniques. For example, officials we interviewed in North  Dakota, which conducted only data matching, said that they lacked  the funding and staff resources to use more advanced techniques.  Officials we interviewed in New Mexico noted that they lacked the  staff resources to use data analytics. Officials from North Dakota said  that they had the option to procure a data analytics tool, but said that  the costs were prohibitively high. Officials in Wisconsin, which was  employing more data analytics, said that they were not able to  purchase access to a third-party data source using SNAP funding  alone, and that they had to seek funding from another federal program  in order to afford these efforts.

	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. FNS provided  assistance through grants, pilot projects, and training at conferences. The  pilot projects also informed FNS’s early efforts to help states improve their  fraud prevention, detection, and investigation processes using data  analytics. Specifically, in recent years, FNS’s assistance to states has  aligned with 4 of the 10 leading practices for data analytics identified by  GAO in its Fraud Risk Framework.

			Ensure Employees Have Sufficient Knowledge, Skills, and Training

In fiscal years 2014 through 2017, FNS conducted a 10-state pilot project  to identify and test promising practices in state fraud prevention and  detection. As part of the project, each participating state received training  and technical assistance in the use of data analytics, in addition to a  review of its business processes. For example, officials from Utah, who  participated in the pilot, said that FNS provided training to them on mining  social media data. The officials added that the timing of the training was excellent because the state was beginning to build its capability for data  analytics on its own. They said that their data analytics team has  incorporated what they learned during the pilot and use various data  analytic techniques every month. As a result, according to officials, the  state’s overpayment collections increased.
In fiscal years 2014 and 2015, FNS awarded nine Recipient Trafficking  Prevention Grants and five Recipient Integrity Information Technology  Grants to a total of 13 states, some of which funded training and staff to  perform SNAP data analytics. For example, in fiscal year 2014,  Kentucky received a grant to purchase and receive training on an analytic  tool with the ability to analyze data and capture posts coming from  various social media sites. In fiscal year 2015, Alaska received a grant  that included 3 months of training related to the installation of the state’s  new fraud case management system that, among other things, would  provide real-time data and automate manual processes to detect fraud  and track cases. According to Alaska’s grant application, this would allow  the state to devote more time to investigations, prosecutions,  recoupment, and analysis and increase the number of completed  investigations.
State officials we interviewed said that they also gained data analytics  knowledge and skills from other states at conference workshops. For  example, officials from North Dakota told us that they attended a  conference presentation in which officials from another state discussed a  performance measure that is designed to assess the savings associated  with detecting SNAP fraud.

			Combine Data Across Programs Within the Agency

FNS has provided grant funding and training to some states to help them  combine data from different databases within the state to facilitate SNAP  data analytics. For example, FNS’s fiscal year 2015 information  technology grants helped five states develop centralized data systems  and consolidate data from multiple outdated systems. Nevada received a  grant to fund the acquisition of a new data system that, according to its  grant application, would combine the state’s data on known SNAP fraud  cases with transaction data and third-party data sets. The data on known  fraud cases would be used to continuously refine data analyses to identify  similar anomalies and patterns in the transaction data. Maine used its  grant to acquire a new investigation case management system that  consolidates data from multiple systems in a centralized repository.  Similarly, New Jersey received a grant to acquire new computer systems  that, according to its grant application, will integrate SNAP case  management system data with data from several of the state’s data  systems, allowing investigators to perform analyses in real time. In  addition to the grants, in fiscal year 2016, FNS sponsored a 5-day course  on fraud detection that demonstrated how states could combine eligibility  data with transaction and other data to identify potential fraud. Officials  from six states participated.

			Pursue Access to External Data and Conduct Data Matching

FNS has provided grants to assist some states in accessing and using  external sources for data matching. For example, in fiscal year 2014, FNS  provided recipient trafficking prevention grants to three states—Florida,  Nevada, and Ohio—to update the systems that they use to match their  SNAP recipients and those that have been disqualified in the state with  FNS’s national database of disqualified recipients. According to FNS,  each grantee state planned to use the funds to link its system with FNS’s  database through the web rather than using a “batch” processing system,  which will allow them to match data on applicants at the time of  application or recertification rather than at specific intervals after eligibility  is determined. Florida officials mentioned in the related grant proposal  that using the state’s current batch processing system meant that other  states did not have real-time access to information about the state’s  disqualified recipients, thereby potentially increasing the chance of an  ineligible individual receiving benefits.
In addition, FNS  administered a grant on behalf of OMB, which funded a  pilot program for five southeastern states to develop the National  Accuracy Clearinghouse (NAC), a data sharing system that allows  participating states to identify applicants who are receiving benefits in the  partnering states in near-real time. According to one state official, a  primary benefit of the NAC is that it enables each participating state to  match data on individual beneficiaries across five states without having to  connect to five different states’ computer systems. One member of the  NAC consortium from Florida said that the ability to match in near-real  time is helpful because the data available in the PARIS system is older  and would only identify individuals potentially receiving benefits in multiple  states months after they have occurred, rather than at the time of  application. As we have previously reported, data on benefit receipts is  updated quarterly in PARIS.

			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.  FNS’s 10-state pilot to test advanced data analytics techniques included  the use of data mining, among other data analytic techniques. One of the  techniques involved mining recipient transaction data for households that  had shopped at disqualified retailers to develop a prioritized list of  retailers and recipients to investigate. According to state officials we  interviewed in Wisconsin, the technique automated a time and labor  intensive process that state analysts had previously performed manually.  The pilot project also used other data mining techniques to develop  profiles of recipients who commit fraud. For instance, in Utah, the data  analysis showed that they are more likely to have multiple replacement  EBT cards and make more purchases from small stores than other  recipients. At the end of the pilot, FNS sponsored a training course that  included detailed instruction on data mining.
Although past efforts by FNS have been limited to some states and  encouraged some leading practices, more recently, in May 2018, FNS  released a SNAP Fraud Framework that provides more comprehensive  guidance to help states adopt all of GAO’s 10 leading practices for data  analytics. Specifically, FNS’s SNAP Fraud Framework provides a  collection of examples, promising practices, and procedures to help state  agencies with the prevention and detection of SNAP fraud that  encompass all 10 data analytics leading practices from GAO’s Fraud Risk  Framework. (For a comparison of the practices in the two frameworks,  see appendix III.) According to FNS officials, the SNAP Fraud Framework  is meant to take a holistic, integrated approach to fraud, including data  analytics, but they recognize that states differ in their readiness to adopt  analytics. The framework’s data analytics section provides a range of  approaches, examples, case studies, and methods that allow all states to  begin embedding analytics into their processes. FNS officials reported  that they began conducting outreach to state officials about the  framework in the summer of 2018. FNS officials said that they are also  considering using grant funds to assist states with the implementation of  components of the framework. Furthermore, FNS officials said that some  of the potential technical assistance may include showing states how to  develop their own analytic tools.
FNS has also developed a maturity assessment to evaluate each state’s  capacity to implement the various components of the fraud framework. It  includes a state’s use of data analytics for fraud detection and  investigations, and its learning and development opportunities for  stakeholders who use the results of data analytics, such as investigators,  hearing officials, and court officials. According to FNS officials, FNS’s  regional offices will conduct maturity assessments as part of management  reviews by the end of fiscal year 2018.

		FNS’s Assistance on Developing Data Analytics Capabilities Has Reached a Limited Number of States

Although FNS has assisted some states in developing their data analytic  capabilities, the methods it has used to do so were meant to reach only a  limited number of states. Specifically, much of FNS’s direct assistance to  states came in the form of pilot projects, competitive grants, or  conferences. 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. Furthermore, FNS’s efforts generally had specific end dates and  did not provide ongoing assistance to reach a broader group of states and  provide them with the knowledge and tools to develop and maintain their  data analytics efforts. (See table 5 for more information on the reach of  FNS’s direct assistance efforts.)
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. Officials we interviewed in five of our seven  selected states said that they attended FNS conferences that provided  training in data analytics and participated in regional discussions on the  topic; however, these events were provided occasionally and limited to  states within the region. State officials said that participating in  conferences in which they could learn from other states’ experiences was  particularly helpful, and they wanted more opportunities to do so. State  officials also told us that it would be beneficial if FNS took a more active  role in disseminating states’ successful practices, particularly with regard  to data analytics. Further communications about data analytics would be  consistent with federal internal control standards that call for agencies to  communicate necessary quality information to external parties in order to  achieve the agency’s objectives. Federal agencies can support external  parties, such as state agencies, in achieving the federal agency’s  objectives by sharing information on effective practices used by the  program or other external parties.
Furthermore, officials we interviewed in selected states most frequently  cited high costs and resource demands as a challenge to using advanced  data analytics techniques. Although FNS has provided some financial  support to state efforts, officials in two states that we reviewed told us that  they were not always able to sustain efforts beyond the life of the FNS  pilot or grant. For example, officials we interviewed from Wisconsin said  that FNS’s contractor for the 10-state pilot, in an effort separate from the  contract, developed a tool that identified SNAP purchases made from  disqualified SNAP retailers. Although the state officials found the tool to  be highly efficient because it could sift through large amounts of data, the  tool was only available to the state for a fee, which they said it could not  afford. Similarly, officials from Washington told us that as part of a  recipient trafficking prevention grant, the state was able to hire two  investigators to detect potential SNAP fraud that may be occurring via  social media. However, according to state officials, the state was unable  to maintain the effort after the grant ended.
In our prior work on establishing data analytic programs to address fraud,  we noted that one way to handle resource challenges is to identify  opportunities that leverage a program’s existing capabilities. In  September 2016, GAO convened a forum of data-analysis experts to  discuss considerations for entities establishing and refining data analytics  programs, during which the costs of such programs were raised.  Panelists, which included officials from FNS, noted that in developing a  data analytics program, an entity should consider ways of leveraging  resources throughout the entity. For example, panelists suggested that an  entity could improve its data analytics group by combining a data  warehouse from one department with existing statistical software from  another and incorporating it with its current fraud-prevention system. The  forum also suggested that a data analytics group should look across the  agency to find staff that may have an interest or experience in working  with data. Panelists noted that such efforts may be improved by seeking  staff from a diverse set of positions and perspectives, including auditors,  evaluators, investigators, and attorneys.
Similarly, some state officials we interviewed shared creative ways to  leverage existing resources. For example, officials from Florida and  Wisconsin stated that they were able to leverage recovered funds from  other programs to purchase access to a commercial database that  matches eligibility data for individuals across related programs. In  Mississippi, officials said that they used SNAP transaction data to identify  individuals living out of state and then determine whether those  individuals were ineligible for both SNAP and other assistance programs.  By combining data and analyses across two programs, the state officials  said that they were able to close more cases and significantly increase  cost savings.
However, other state officials noted that leveraging resources, especially  data, poses challenges that states will need to learn how to resolve.  Specifically, some states reported facing problems sharing data across  different systems and with restrictions on sharing sensitive personal  information. For example, officials representing four states from the  American Association of SNAP Directors (AASD) told us that, for states to  leverage data, SNAP states’ data systems need to be integrated across  states. However, in their view, the cost of integration may exceed the  benefits from integrating the data. In addition, state officials said that in  order to leverage personal data, some states as well as programs in the  same state will need to reach agreements that define how data will be  extracted and used while protecting privacy. For example, a Wisconsin  official told us that its data analytics group has difficulty acquiring data  across programs within the state because of confidentiality and privacy  rules as well as the difficulty of reaching data-sharing agreements with  other programs.
Moving forward, FNS’s SNAP Fraud Framework, combined with its  maturity assessment, will form the core of FNS’s efforts to assist states  with data analytics in a broad-based, systematic manner. According to  FNS officials, the agency will be conducting outreach to states about the  fraud framework and assessing both states’ capacities in data analytics  and barriers to gaining the necessary knowledge and tools for developing  and maintaining those efforts.

	Conclusions

To ensure that SNAP funds are used for the purposes for which they  were intended, both the federal government and state agencies should  have appropriate controls for detecting and addressing fraud. The use of  data analytics, such as mapping and predictive analysis, may help SNAP  agencies increase program integrity and improve administrative  efficiency. Data mining and data matching techniques can help identify  potential SNAP fraud, and predictive models can help identify  characteristics of SNAP traffickers. Our use of analytics on SNAP out-of- state transaction data from three selected states identified only slight  differences between those households who shopped out of state and  those who did not, suggesting that analyses of other data elements that  have been shown to be indicative of potential trafficking may allow states  to better identify potential trafficking and, thereby, better target resources.
Although FNS has efforts underway to promote the use of data analytics  to improve SNAP fraud detection through its fraud framework and  maturity assessment, officials in our selected states cited challenges with  accessing and maintaining needed resources such as staff, technology,  and tools. While these challenges may limit states’ ability to implement  data analytics, some of our selected states have successfully overcome  such challenges to implement or enhance data analytics programs. For  example, two states described leveraging recovered funds and  reinvesting them to combat fraud. Another state leveraged transaction  data across two programs, resulting in financial savings and enhanced  collections, which could be reinvested to combat fraud. As FNS conducts  outreach to help states implement its fraud framework and uses its  maturity assessment to assess states’ anti-fraud capabilities, it has an  opportunity to regularly assist states with adopting advanced data analytic  techniques. Based on the experiences described by state officials, finding  ways that states can leverage existing resources to improve their data  analytic capabilities may be an important part of any solution. In its role as  the federal oversight agency, FNS is in a position to collect and widely  disseminate information about those states that have built support for  data analytics and leveraged existing resources to implement or expand  their data analytics programs to states seeking such examples. With  wider dissemination of these examples of state successes, all state SNAP  agencies could be better positioned to enhance their own efforts to  identify and address SNAP fraud.

	Recommendation for Executive Action

Building on ongoing efforts, the Administrator of FNS should develop and  implement additional methods to widely distribute information to state  agencies on an ongoing basis about successful efforts to adopt data  analytics and strategies to leverage existing data, technology, and staff  resources to enhance data analytics. (Recommendation 1)

	Agency Comments and Our Evaluation

We provided a draft of this product to the U.S. Department of Agriculture  for comment. In oral comments on September 14, 2018, FNS officials  from SNAP’s Program Accountability and Administration Division and the  Deputy Associate Administrator for SNAP agreed with our  recommendation. They noted that they have been moving in the general  direction of this recommendation and would build on current efforts to  address it but noted that state readiness and technical capabilities are  limiting factors in the adoption of data analytics. FNS also provided  technical comments, which were incorporated into the report as  appropriate.
As agreed with your offices, unless you publicly announce the contents of  this report earlier, we plan no further distribution until 30 days from the  report date. At that time, we will send copies to relevant congressional  committees, the Secretary of Agriculture, the FNS Administrator, and  other relevant parties. In addition, the report will be available at no charge  on the GAO website at http://www.gao.gov.
If you or your staff have any questions about this report, please contact  us at (202) 512-7215 or LarinK@gao.gov or (202) 512-6722 or  BagdoyanS@gao.gov. Contact points for our Offices of Congressional  Relations and Public Affairs may be found on the last page of this report.  GAO staff that made key contributions to the report are listed in appendix  IV.

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 address these objectives, we  primarily focused on federal and state SNAP recipient anti-fraud work  since the beginning of fiscal year 2015—the period which follows our  August 2014 report on SNAP recipient fraud. We reviewed relevant  federal laws, regulations, program guidance, and reports, and we  interviewed FNS officials in headquarters and all seven regional offices to  address all three objectives and obtained relevant documentation.
To assess the extent that SNAP households in selected states made out- of-state purchases that may indicate potential recipient fraud, we  analyzed all out-of-state purchase data nationwide and we analyzed  transaction data for SNAP households in the District of Columbia and two  states–North Dakota and Washington. We selected these states as they  were among the top states for out-of-state spending in a non-border state  in fiscal years 2015 and 2016, the two most recent years’ of SNAP data  available when we started this review. We obtained SNAP transaction  data from FNS for all participating households in the three selected  states, and analyzed fiscal year 2017 data for households that spent all  their benefits in a non-border state in that year. We also analyzed fiscal  year 2017 data for all households in these three states for purchases that  may indicate trafficking, based on common suspicious transaction types.  We tested the transaction data for ten different suspicious transaction  types that have been used by FNS and state SNAP officials to identify  potential trafficking. While the transactions we flagged for potential  trafficking in our three selected states are generally deemed potential  indicators of fraud by SNAP officials, there could also be legitimate  reasons for these purchases and they do not prove trafficking. For that  reason, our analysis focused on households with a greater frequency of  questionable purchases in fiscal year 2017 indicating potential  trafficking—specifically purchases that resulted in 20 or more trafficking  flags. We assessed the reliability of SNAP transaction data used in  analyses through review of related documentation, interviews with  knowledgeable officials, and electronic testing of the data, and found  them to be sufficiently reliable for our purposes.
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. We obtained related  documentation when available. We selected the District of Columbia,  Massachusetts, Mississippi, New Mexico, North Dakota, Washington, and  Wisconsin to reflect a range of experiences based on the percentage of  non-border state transactions, receipt of related technical assistance,  geographic region, and FNS’s reports on their capacity to conduct data  analysis. We interviewed state SNAP agency officials who oversee anti- fraud practices in each of our seven selected states. During each  interview, we collected information on each state’s data analytics  activities and whether they have implemented leading practices for data  analytics from GAO’s Fraud Risk Framework. We also discussed the  advantages and challenges of using data analytics. While information  from these seven state SNAP agencies is non-generalizable, it provided  illustrative examples of agencies’ efforts to use data analytics.
To determine the degree to which FNS has assisted states in developing  the use of data analytics, we reviewed grant documentation FNS awarded  to states to help prevent recipient trafficking or improve technology used  to improve program integrity. We also reviewed the terms of work for a  contract FNS awarded to a private consulting firm to conduct a pilot  project with 10 states during fiscal years 2014-2017, as well as reports  delivered by the contractor detailing the results of the work. In addition,  we reviewed a guide to data analytics that FNS developed for a 5-day  training session in August 2016, as well as the data analytics “maturity  assessment” questionnaire that is intended for FNS regions to use to  assess the capacity of the states. We also obtained and reviewed FNS’s  SNAP Fraud Framework and Supplementary Materials that was released  in May 2018. After developing an inventory of how FNS has assisted  states in assessing and developing its data analytic capacity, we  analyzed FNS’s actions with respect to GAO’s set of leading practices for  data analytics from GAO’s Fraud Risk Framework and GAO’s standards  for internal control. We also analyzed FNS’s SNAP Fraud Framework to  assess the degree to which it addressed GAO’s leading practices on how  to use data analytics to detect, prevent, and investigate SNAP fraud.  Unless specified, we reviewed only data analytic activities that occurred  since the beginning of fiscal year 2015, which marks the end of our  previous analysis of FNS’ anti-fraud activities concerning the SNAP  program. To obtain FNS’ views, we interviewed SNAP program officials at  both headquarters and at each of SNAP’s seven regional offices. To  obtain a broader perspective on the use of data analytics across states,  we interviewed officials representing the American Association of SNAP  Directors (AASD) and the United Council on Welfare Fraud (UCOWF).  AASD representatives included officials from the SNAP anti-fraud units  for California, New York, Tennessee, and Texas. UCOWF  representatives included officials from Florida, Louisiana, and Utah. In  addition, we interviewed the Deputy Executive Director of American  Public Human Services Association, AASD’s parent organization, and  officials representing USDA’s Office of Inspector General.
We conducted this performance audit from May 2017 through October  2018 in accordance with generally accepted government auditing  standards. Those standards require that we plan and perform the audit to  obtain sufficient, appropriate evidence to provide a reasonable basis for  our findings and conclusions based on our audit objectives. We believe  that the evidence we obtained provides a reasonable basis for our  findings and conclusions based on our audit objectives.

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. States whose SNAP recipients  had the highest percent of out-of-state purchases included Delaware,  District of Columbia, Idaho, Nebraska, New Mexico, Rhode Island, South  Dakota, Tennessee, Vermont, and West Virginia. All of these states made  at least 5 percent of total purchases out of state. The states with the  lowest percent of out-of-state spending by SNAP recipients included  Alaska, California, Florida, Hawaii, Michigan, and Texas (see fig. 5).  Detailed information on out-of-state spending by SNAP recipients, by  state, is also provided in table 6 below.

Appendix III: Leading Practices for Data Analytics and FNS’s 2018 SNAP Fraud Framework Comparison

In May 2018, FNS released a fraud framework that provides guidance to  help states adopt all of GAO’s leading practices for data analytics. The  table below compares guidance in FNS’s SNAP Fraud Framework to the  leading practices in GAO’s Fraud Risk Framework.

Appendix IV: GAO Contacts and Staff Acknowledgments

	GAO Contacts

	Staff Acknowledgments

In addition to the contacts named above, the following staff members  made key contributions to this report: Danielle Giese and Philip Reiff,  Assistant Directors; Celina Davidson and Lara Laufer, Analysts-in- Charge; Camille A. Keith; Kelly Snow; and Daren Sweeney. Also  contributing to this report were Susan Aschoff, James Bennett, Alexander  Galuten, James Murphy, Almeta Spencer, and Shana Wallace.

Output:
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.