Background Direct Loan Program and Repayment Plans The Direct Loan program provides financial assistance to students and their parents to help pay for postsecondary education. Under the Direct Loan program, Education issues several types of student loans (see sidebar). Current William D. Ford Federal Direct Loan Types Subsidized Stafford Loans: Available to undergraduate students with financial need (generally the difference between their cost of attendance and a measure of their ability to pay, known as expected family contribution). Borrowers are not responsible for paying interest on these loans while in school and during certain periods of deferment, an option that allows eligible borrowers to temporarily postpone loan payments. Unsubsidized Stafford Loans: Available to undergraduate and graduate school students irrespective of financial need. Borrowers must pay all interest on these loans. PLUS Loans: Available to graduate student borrowers and parents of dependent undergraduates. Borrowers must pay all interest on these loans. Consolidation Loans: Available to eligible borrowers wanting to combine multiple federal student loans (including those listed above) into one loan. Repayment periods are extended up to a maximum of 30 years, thereby lowering monthly payments. Interest rates for these loans are tied to the Department of the Treasury’s 10-year note rate and can vary by loan type. In addition, there are limits on the annual and aggregate amounts that can be borrowed for certain loan types. After a prospective borrower applies for and is awarded a Direct Loan, Education disburses it through the borrower’s school. Once the loan is disbursed, it is assigned to one of nine loan servicers under contract with Education. These loan servicers are responsible for such activities as communicating with borrowers about the status of their loans, providing information about and enrolling borrowers in repayment plans, and processing payments. Once borrowers leave school, they are responsible for making payments directly to their assigned loan servicer. A variety of repayment plans are available to eligible Direct Loan borrowers, including Standard, Graduated, Extended, and several IDR plans. Borrowers are automatically enrolled in the Standard plan if they do not choose another option, and generally make fixed monthly payments over a period of 10 years. IDR plans can ease repayment burden by setting monthly loan payments based on a borrower’s income and family size and extending the repayment period up to 20 or 25 years, depending on the plan. Unlike Standard, Graduated, and Extended repayment plans, IDR plans offer loan forgiveness at the end of the repayment period and monthly payments may be as low as $0 for some borrowers. There are a variety of IDR plans, and these plans have differences in eligibility requirements, how monthly payment amounts are calculated, and repayment periods before potential loan forgiveness (see table 1). Application Process for Income-Driven Repayment Plans To participate in an IDR plan, borrowers must submit an application to their loan servicer that, among other things, includes information about their income, marital status, and family size (see table 2). According to Education, Education’s loan servicers review the information borrowers submit on their IDR applications to determine if borrowers are eligible for IDR plans. If the servicer determines that a borrower is eligible, it enrolls the borrower in an IDR plan and calculates the borrower’s monthly payment amount. To continue making monthly payment amounts based on income and family size, IDR borrowers must annually submit the IDR application form certifying their income and family size, which servicers then use to update monthly payment amounts. If a borrower’s income changes significantly prior to the borrower’s annual recertification date, the borrower can use the same application form to request a recalculation of the monthly payment amount (see fig. 1). However, borrowers are not required to report any such changes before their annual recertification date. If IDR borrowers do not have any discretionary income, their scheduled monthly payment amount is zero dollars (meaning they will not have to make a monthly loan payment until their discretionary income is high enough to warrant one). Scheduled monthly payments of zero dollars count as qualifying payments towards eventual loan forgiveness at the end of the 20- to 25-year repayment period. Borrowers who make monthly payments on IDR plans that are much lower than they would be under the Standard 10-year repayment plan for a long period of time may end up paying less than their original loan amount because their remaining loan balances may be forgiven. However, some borrowers on IDR plans will fully repay their loans before qualifying for forgiveness. Extending the repayment period may also result in some borrowers paying more interest over the life of the loan than they would under the 10-year Standard repayment plan. Standards and Guidance for Managing Risk of Fraud and Errors in Federal Programs Fraud in federal programs occurs when individuals or entities intentionally misrepresent themselves in order to benefit from the programs. Fraud poses a significant threat to the integrity of federal programs and erodes public trust in government. Federal programs are at risk for fraud when individuals have both the opportunity and incentive to commit fraud. Although the occurrence of one or more cases of fraud indicates there is a fraud risk, a fraud risk can exist even if fraud has not yet been identified or occurred. Proactive fraud risk management is meant to facilitate a program’s mission and strategic goals by ensuring that taxpayer dollars and government services serve their intended purposes. In July 2015, GAO issued the Fraud Risk Framework, which provides a comprehensive set of components and leading practices that serve as a guide for agency managers to use when developing efforts to combat fraud in a strategic, risk-based way. The Framework recommends that to effectively manage fraud risks, managers should design and implement specific control activities to prevent and detect potential fraud, such as data analytics. After issuance of the Fraud Risk Framework, the Fraud Reduction and Data Analytics Act of 2015 was enacted to improve federal agency controls and procedures to assess and mitigate fraud risks, and to improve agencies’ development and use of data analytics for the purpose of identifying, preventing, and responding to fraud. The act requires agencies to establish financial and administrative controls that incorporate the Fraud Risk Framework’s leading practices. We previously reported that Education identified itself as subject to the act. Error also poses a risk to the integrity of federal programs. According to federal internal control standards, to maintain an effective internal control system, managers should use quality information to achieve agency objectives. This includes obtaining information from reliable sources that is reasonably free from errors and communicating it externally to achieve objectives. Indicators of Potential Fraud or Error in Income and Family Size Information Pose Risks to IDR Plans Over 95,000 IDR Plans Were Held by Borrowers Reporting No Income, but Data Suggests They May Have Had Enough Wages to Make Student Loan Payments Our analysis of Education’s IDR plan data and HHS’s NDNH wage data for borrowers who reported zero income found that about 95,100 approved IDR plans (11 percent of all IDR plans we analyzed) were held by borrowers who may have had sufficient wages to warrant a monthly student loan payment. These plans were held by about 76,200 unique borrowers who owed nearly $4 billion in outstanding Direct Loans as of September 2017. According to our analysis, 34 percent of these plans were held by borrowers who had estimated annual wages of $45,000 or more, including some with estimated annual wages of $100,000 or more (see fig. 2). Our results from matching the Education and HHS data indicate the possibility that some borrowers misrepresented or erroneously reported their income, highlighting the risk of potential fraud and errors in IDR plans. Borrowers may have a financial incentive to commit fraud to reduce their monthly payment amount and, by extension, possibly increase the amount of loan debt forgiven at the end of their repayment periods. However, we cannot determine whether fraud occurred through data matching alone. Where appropriate, we are referring these results to Education for further investigation. Among the 76,200 borrowers in our data matching results, it is possible that some accurately reported zero income even though they had wages reported in NDNH in the same quarter in which their IDR application was approved. For example, a borrower may have earned wages at the start or end of a quarter, but was not earning wages at the time of submitting the IDR application. Conversely, our analysis cannot identify borrowers who may have earned additional taxable income that is not part of NDNH data, but should be included on IDR applications, such as income for individuals who are self- employed or receiving alimony. Regarding the potential for error, officials from Education and all four loan servicers we spoke with stated that it is possible that borrowers could incorrectly report that they had no taxable income. Officials from Education said, for example, that borrowers may misunderstand the question about taxable income on the IDR application, and one loan servicer, echoing this perspective, stated that some borrowers may mistakenly think that some of their income is nontaxable when it is in fact taxable. To examine how borrowers’ failure to report their income could affect the amount repaid to Education over the course of a year, we used Education’s online repayment estimator to illustrate how much hypothetical borrowers with different annual adjusted gross incomes would expect to pay under each IDR plan (see fig. 3). If a borrower at one of these income levels instead reported zero income on the IDR application, Education could lose thousands of dollars per borrower each year in student loan payments. Such a situation could also potentially increase the ultimate cost to the federal government and taxpayers for loan forgiveness because scheduled monthly payments of zero dollars count toward the borrower’s 20- or 25-year repayment period. Education May Miss Indicators of Potential Fraud or Error in Borrowers’ Family Sizes To examine the extent to which Education’s IDR plan data on family size may indicate potential fraud or error, we analyzed the family sizes for about 5 million IDR plans approved between January 1, 2016 and September 30, 2017. Of these plans, over 2.1 million (43 percent) were approved with a family size of one, meaning only the borrower was included (see fig. 4). In addition, over 2.6 million plans (52 percent) were approved with family sizes of two to five. At the high end of the spectrum, about 40,900 of the plans we analyzed (about 1 percent) were approved with family sizes of nine or more (see fig. 5). We consider IDR plans with family sizes of nine or more atypical or outliers because they comprise the top 1 percent of all family sizes in Education’s data. Of these plans, almost 1,200 had family sizes of 16 or more, including two plans held by different borrowers that were approved with a family size of 93. In total, the 40,900 plans approved with family sizes of nine or more corresponded to about 35,200 unique borrowers who owed almost $2.1 billion in outstanding Direct Loan debt as of September 2017. While IDR plans with family sizes of nine or more were atypical in our data and could indicate fraud or error, IDR plans with smaller or more typical family sizes could also pose problems. Borrowers may have a financial incentive to commit fraud because larger family sizes reported on the IDR application can reduce borrowers’ discretionary income and, by extension, their monthly payment amounts. Regarding the potential for error, officials from Education and all four loan servicers we spoke with said borrowers or loan servicers may inadvertently make mistakes related to family size. For example, officials from Education and one loan servicer said borrowers sometimes report inaccurate family sizes if they are confused about who to count as a member of their family. Officials from this loan servicer told us that a borrower initially applied for an IDR plan claiming a family size of five— himself and four other family members who were not his spouse or children. They said that during a subsequent phone call with loan servicer staff about the borrower’s loan, the borrower volunteered that the other members of his family did not live with him, meaning that for IDR purposes, he had a family size of one. It is unclear whether this borrower may have misrepresented his family size to receive a lower monthly payment or did not understand the definition and reported it in error. In regards to loan servicer error, Education officials said that servicers may make mistakes when entering family sizes from paper applications into their computer systems or when determining the total family size because borrowers provide information on family members in up to three places on the application. To examine the effect of family size on monthly payment amounts in IDR plans, we used Education’s online repayment estimator to illustrate how much hypothetical borrowers with the same income but different family sizes would be expected to pay each month under certain IDR plans. We found that a hypothetical borrower with a family size of one and an adjusted gross income of $40,000 who enrolls in one of three IDR plans that base monthly payment amounts on 10 percent of discretionary income would have a monthly payment amount of $182 (see fig. 6). If this borrower instead reported a family size of two people, the monthly payment amount would decrease by $54, to $128. For each additional person, the monthly payment would decrease by $54. At a family size of five people, the borrower would have no monthly payment. Weaknesses in Education’s Procedures to Verify Income-Driven Repayment Plan Information Reduce Its Ability to Detect Potential Fraud or Error, but Approaches Exist to Address Risks Education Does Not Verify Borrower Reports of Zero Income and Has Limited Protocols for Verifying Borrower Family Size Education does not have procedures to verify borrower reports of zero income nor, for the most part, procedures to verify borrower reports of family size; although there are approaches it could use to do so. Because income and family size are the basis for calculating borrowers’ monthly payment amounts for IDR plans, it is important that this information is accurate on IDR applications. While Education instructs loan servicers to review tax returns and other documentation of taxable income that borrowers are required to provide, as previously discussed, borrowers are not required to provide documentation to support self-attestations of zero income or their family size on IDR applications. Officials from Education and all four loan servicers we spoke with said that servicers are generally instructed to take these self-attestations at face value. However, Education has limited, voluntary procedures for reviewing family size information submitted by borrowers. In 2016, Education implemented a voluntary procedure for loan servicers to contact borrowers who report changes in family size of four or more from one year to the next in order to verify the accuracy of the most recently reported family size. Education officials told us that servicers are not contractually required to follow this procedure. In addition, this procedure is not applicable to student loan borrowers when they initially apply for IDR plans. In October 2018, Education officials told us they began to follow up with loan servicers about family sizes of 20 or more in IDR program data to ensure these data match the family size information in the loan servicer systems from which they originated. Officials said that this process is to ensure that family size data were accurately transferred from servicers to Education. Borrowers are not contacted for verification of the information itself. Officials from Education and three of the four loan servicers we spoke with acknowledged that IDR plans are at risk for fraud or error because verification is generally not performed on borrower reports of zero income and borrower reports of family size. Officials from Education and two of the loan servicers also said that certain program requirements discourage borrowers from providing false information. For example, borrowers are required to sign the IDR form to certify that all provided information is true, complete, and correct, and the form warns borrowers that false statements or misrepresentations are subject to penalties including fines, imprisonment, or both. However, the extent to which this requirement may serve as a deterrent is unknown because Education has not assessed the risk of fraudulent reports on IDR applications. Moreover, Education officials told us that they were not aware of any IDR borrowers being investigated or facing penalties for providing false information on the IDR application. Officials from one loan servicer also said that borrowers may be deterred from falsely claiming zero income or misrepresenting their family size because they assume that servicers, acting on behalf of the government, can check the information on IDR applications. However, it is also possible that borrowers would assume that this self-reported information would not be routinely verified because the only documentation requirements discussed on the application relate to verifying taxable income. Education officials also said that the risk of borrowers providing inaccurate information on IDR applications must be balanced against the impact of adding verification procedures. They said additional procedures could make the already complex IDR application process more burdensome for borrowers to navigate and result in longer application processing times. While it is important to make IDR plans accessible to borrowers who could benefit from them, it is also important that Education design internal control activities to achieve program objectives and respond to risks, including addressing the risk of fraud and error in borrower self-reported information. GAO’s Fraud Risk Framework describes the importance of developing procedures for preventing, detecting, and responding to the risk of fraud in government programs. The risk of fraud exists when there is opportunity and incentive to commit it. The lack of verification of borrower reports of zero income and limited verification of borrower reports of family size on IDR applications creates the opportunity for borrowers to commit fraud. Because lower income and larger family sizes can reduce borrowers’ monthly payment amounts and, by extension, possibly increase the amount of loan debt forgiven at the end of their repayment periods, there is also an incentive for some borrowers to commit fraud. In regard to error, federal internal control standards state that agencies should obtain information from reliable sources that are reasonably free from error. Education officials and all four loan servicers told us that borrower-reports of family size or zero income can be susceptible to error if, for example, borrowers misunderstand the definitions of these items on IDR applications. Addressing the risk of fraud and error would also help to minimize the costs associated with IDR plans that are passed on to the government and taxpayers. As more borrowers enter IDR plans, the costs of these plans—including loan forgiveness—increase for the government and taxpayers. Using data underlying the President’s fiscal year 2017 budget request, GAO previously reported that Education estimated Direct Loans repaid with IDR plans would cost the federal government about $74 billon over their repayment periods. In its fiscal year 2015-2019 strategic plan for Federal Student Aid, Education acknowledged that as IDR plans continue to grow in popularity, the cost of loan forgiveness could be a major issue for the federal government. Education can minimize the costs associated with IDR plans by ensuring payment amounts are based on accurate income and family size information. Approaches Exist That Could Help Education Identify and Address Potential Fraud or Error in IDR Plans Education has not fully leveraged available approaches to help detect and prevent fraud or error in IDR plans. Federal internal control standards call for agency management officials to identify, analyze, and respond to risks related to achieving program objectives, such as the risk of using potentially fraudulent or erroneous information about borrowers to calculate monthly payment amounts for student loans. Approaches, such as using data analytic practices and follow-up procedures, can help identify and address these risks. Two data analytic practices that can help identify such risks with respect to IDR plans are (1) anomaly detection to identify atypical or unusual information about borrowers and (2) data matching with outside data sources to verify information that borrowers provide. These practices, which can be used on their own or together, can help prevent fraud from occurring and detect potential fraud or error that may have occurred. Because data analytics alone may not be sufficient to determine whether fraud or error has occurred, follow-up procedures can then be used in the investigation and verification to make such determinations. A leading practice in data analytics in GAO’s Fraud Risk Framework is conducting data mining to identify suspicious activity or transactions, including anomalies, outliers, and other red flags in the data. Similar to our family size analysis, borrower-reported family sizes above a certain threshold on IDR applications could be flagged in loan servicers’ and Education’s data systems for further verification. Anomaly detection is used to a limited extent to identify errors in family size on IDR plans by one loan servicer and by Education. According to officials at Education and all four loan servicers we spoke with, anomaly detection is not used to systematically identify potentially fraudulent reports of family size. Anomaly detection can also identify deviations from expected patterns in data over time. Because IDR borrowers are required to fill out applications annually, it would be possible to develop automated queries to look for unusual patterns in borrower-reported income and family size from one year to the next. Officials from Education and servicers described several patterns across applications that could indicate potential fraud, specifically large swings in income from one year to the next, reporting zero income for multiple years, and having a large family size, but relatively low income. Another leading practice for data analytics in GAO’s Fraud Risk Framework is conducting data matching to verify key information, including self-reported data and information necessary to determine eligibility. The results of our analysis illustrate the usefulness of this technique to identify potential inconsistencies in the income information on IDR plans. Education does not have authority to access wage data from HHS’s NDNH or income data from the Internal Revenue Service (IRS) for the purpose of verifying IDR borrowers’ income information through data matching. However, private data sources are also available for data matching. We reported in 2016 on the benefits of government agencies using private data to address the risk of fraud. Moreover, some state agencies (such as those administering the Supplemental Nutrition Assistance Programs) use a private, commercial verification service known as The Work Number® to help determine eligibility for government assistance. We reported in 2016 that 45 states used income information from The Work Number to help determine eligibility for food assistance benefits under the Supplemental Nutrition Assistance Program. Education may also be able to draw on follow-up procedures it has in place for verifying information submitted by students and their families when applying for federal student aid using the Free Application for Federal Student Aid (FAFSA). Education uses a process called “verification” to help identify and correct erroneous or missing information on the application to aid the department’s efforts to reduce improper payments of federal student aid. Each award year, a portion of FAFSA applications are selected for verification, and schools are required to work with the selected applicants to obtain documentation and confirm the accuracy of information provided on these applications. When selecting FAFSAs for verification, Education aims to select those applications with the highest statistical probability of error and the greatest impact of such error on award amounts. FAFSA applicants who are selected to verify their income for the 2018-2019 or 2019-2020 award years may provide a signed copy of their prior years’ tax returns. FAFSA applicants may also obtain documentation from the IRS through the IRS Data Retrieval Tool, an IRS tax return transcript, or an IRS Verification of Non-filing Letter. FAFSA applicants selected to verify their household size must provide a signed statement that provides the name, age, and relationship to the student of each person in the household. For IDR plans, Education could implement follow-up procedures for IDR applications it identifies as at risk for fraud or error and seek additional documentation from borrowers. For example, to verify reports of no income, borrowers could be asked to provide an IRS Verification of Non- filing Letter, documentation that the borrower recently lost a job, or documentation that shows income the borrower receives is nontaxable, such as public assistance benefits. To verify family size, as is the case with FAFSA verification, borrowers could be asked to provide a signed statement with the names, ages, and relationship to the borrower of each family member. Another option might be to request that borrowers provide documentation showing that family members (other than the borrower’s spouse and children) receive mail at the borrower’s address as well as documentation of the financial support provided by the borrower. Such follow-up procedures would be consistent with federal internal control standards advising managers to design control activities to achieve program objectives and respond to risks. Conclusions While Income-Driven Repayment plans can help borrowers with limited incomes afford their monthly student loan payments, these plans can also result in high costs to the federal government and taxpayers. To minimize these costs, it is important that Education accurately determine monthly payment amounts under its IDR plans. Because these determinations are based on income and family size information that borrowers self-report, there is risk for potential fraud or error. Our data matching analysis showed, for example, that tens of thousands of borrowers who were not making monthly loan payments because they reported zero income on IDR applications may have had enough income to do so. Where appropriate, we are referring these borrowers to Education for further investigation. In addition, an increase in family size can cause a borrower’s payments to decrease, creating a potential incentive for fraud, and our analysis found atypically large family sizes that are generally not verified by Education. The results of our analyses highlight the risk for fraud or error, as well as weaknesses in Education’s procedures. In turn, the weaknesses we identified raise questions about the strength of Education’s institutional oversight of a major program involving hundreds of billions of dollars. The fact that, cumulatively, the borrowers and their plans we reviewed owed over $6 billion in loans helps illustrate the risk of potential financial loss for the government from fraud or error absent comprehensive oversight. It is important for Education to take steps to obtain data to verify borrower reports of zero income and to implement other data analytic practices and follow-up procedures for verifying borrower-reported information. Such actions would help ensure that (1) IDR payment amounts are based on information that accurately represents a borrower’s situation and is free from fraud and error; and (2) the federal government’s fiscal exposure to IDR loans is safeguarded from the risk of loss. Implementing data analytic practices and follow-up procedures to review and verify borrower reports of zero income could help deter borrowers from inaccurately reporting zero income and detect those who have done so, either fraudulently or in error. Similarly, implementing practices and procedures to review and verify reported family sizes could further stem potential fraud or error. Without such changes, IDR plans will remain vulnerable to fraud and error, potentially raising program costs for the federal government and taxpayers. Recommendations for Executive Action We are making the following three recommendations to Education’s Federal Student Aid office: The Chief Operating Officer of Federal Student Aid should obtain data in order to verify income information for borrowers reporting zero income on IDR applications. For example, Education could pursue access to federal data sources or obtain access to an appropriate private data source. (Recommendation 1) The Chief Operating Officer of Federal Student Aid should implement data analytic practices, such as data matching, and follow-up procedures to review and verify that borrowers reporting zero income on IDR applications do not have sources of taxable income at the time of their application. (Recommendation 2) The Chief Operating Officer of Federal Student Aid should implement data analytic practices, such as data mining, and follow-up procedures to review and verify family size entries in IDR borrower applications. For example, Education could review and verify all borrower reports of family size or a subset identified as being most susceptible to fraud or error. (Recommendation 3) Agency Comments and Our Evaluation We provided a draft of this report to the Departments of Education (Education) and Health and Human Services (HHS) for review and comment. HHS provided technical comments, which we incorporated as appropriate. We also provided relevant report sections to the Social Security Administration and the four loan servicers included in our review for technical comments. Loan servicers provided technical comments, which we addressed as appropriate. Education generally agreed with our recommendations, stating that it plans to implement significant additional verification policies to ensure that borrowers who participate in IDR plans do not misrepresent their income or family size to the department. While Education agreed with our recommendation to obtain data in order to verify income for borrowers reporting zero income, it suggested that GAO may wish to convert this recommendation to a Matter for Congressional Consideration to provide Education with access to IRS data. In its response, Education stated that the President’s fiscal year 2020 budget request includes a proposal that Congress pass legislation allowing the IRS to disclose tax return information directly to the department for the purpose of administering certain federal student financial aid programs. According to Education, such legislation, if enacted, would allow borrowers to more easily certify their income on an annual basis to maintain enrollment in IDR plans, and allow the department to use the information to mitigate improper payments to borrowers as a result of misreported income data. Education also stated that in the meantime, it would explore whether commercially available data are sufficient in terms of scope, reliability, and cost effectiveness. Given that there are existing actions Education can take to implement our recommendation, we believe our recommendation is appropriate. Moreover, we believe that Education is best positioned to determine whether the proposal, if enacted, would address our recommendation, or if it would need to be expanded or modified in order to do so. Regarding our second recommendation, Education stated that it would develop data analytic practices to verify borrower reports of zero income contingent upon the enactment of legislation providing the department with access to federal income data. However, implementing our recommendation does not necessarily require Education to wait for such legislation. Our draft report describes data analytic practices, such as anomaly detection, which Education could implement using its own data to identify deviations from expected patterns in data over time. Education also stated that it plans to develop additional follow-up procedures to verify borrower reports of zero income, such as requiring borrowers to substantiate reports of zero income with appropriate documentation. In addition, Education described plans to formalize procedures to make referrals to Education’s Office of Inspector General or the Department of Justice for suspected cases of IDR fraud. We encourage Education to combine its follow-up procedures with data analytic practices to satisfy the recommendation. Education agreed with our third recommendation to implement data analytic practices and follow-up procedures to verify family size, noting that this information could be subject to misrepresentation or erroneous reporting by borrowers. Education stated that it would review various data points that can be used to select IDR applications and certifications for additional review prior to approval, such as providing more scrutiny when borrowers report unusual increases in family size from one year to the next. Education also stated that it plans to formalize additional procedures to require certain borrowers to substantiate their family size. For example, Education will consider requiring IDR applicants to provide statements listing each household member and how they are related to the borrower. 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 Education, the Chief Operating Officer of Federal Student Aid, 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 (617) 788-0534 or emreyarrasm@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 III. Appendix I: Objectives, Scope, and Methodology This report examines (1) whether there are indicators of potential fraud or error in income and family size information provided by borrowers seeking to repay their loans with Income-Driven Repayment (IDR) plans and (2) the extent to which the Department of Education (Education) verifies this information. To address these questions, we reviewed relevant IDR policies and procedures from Education and its four largest loan servicers—Navient, Nelnet, Great Lakes Educational Loan Services, Inc., and the Pennsylvania Higher Education Assistance Agency. We selected these loan servicers because, at the time of our analysis, together they serviced 96 percent of the outstanding balance of loans being repaid with IDR plans as of September 2017. We also interviewed Education officials from Federal Student Aid, the office responsible for developing policies and procedures for administering IDR plans and overseeing how loan servicers carry them out, as well as the officials from the selected loan servicers. Additionally, we reviewed relevant federal laws and regulations and Education’s procedures for verifying information on the Free Application for Federal Student Aid. We assessed Education’s procedures against federal standards for internal control for developing sufficient control activities, risk assessment, and information and communication. We also assessed Education’s procedures against the leading practices for data analytics activities in GAO’s Framework for Managing Fraud Risks in Federal Programs. To determine whether there were indicators of potential fraud or error in borrowers’ income and family size information on IDR plans, we obtained data from Education’s Enterprise Data Warehouse and Analytics (EDWA) database on borrowers with William D. Ford Federal Direct Loans (Direct Loans) and IDR plans approved between January 1, 2016 and September 30, 2017, the most recent data available at the time of our analysis. EDWA is a centralized data warehouse that contains administrative data reported by loan servicers on IDR borrowers and their loans. Some borrowers had multiple approved IDR plans in the data we analyzed. We also obtained national quarterly wage data from the U.S. Department of Health and Human Services’ (HHS) National Directory of New Hires (NDNH) for the same time period. NDNH is a national repository of information reported by employers, states, and federal agencies. The NDNH is maintained and used by HHS for the federal child support enforcement program, which assists states in locating parents and enforcing child support orders. In addition to information on newly hired employees, NDNH contains (1) data on quarterly wages for existing employees, collected and reported by state workforce agencies and federal agencies; and (2) data on all individuals who apply for or received unemployment compensation, as maintained and reported by state workforce agencies. For our analysis of borrower-reported incomes, we matched approximately 656,600 Education borrowers to NDNH quarterly wage data to determine if any borrowers who reported zero income on their IDR applications had wages reported in the same quarter in which their IDR plans were approved. We took additional steps to further review and refine these matches and provide reasonable assurance that the NDNH wage data were associated with the correct borrower by comparing (1) the borrower’s state of residence as reported in the Education data to the state agency submitting the NDNH wage data and (2) the borrower’s name as reported in the Education data to the employee name reported in the NDNH data. For the refined matches, we then estimated whether the borrowers may have had sufficient annual wages based on wages reported in NDNH to potentially warrant monthly student loan payments greater than zero dollars on their associated IDR plan. Specifically, we aggregated all NDNH wages reported for the borrower in the quarter in which their IDR plan was approved to determine a total quarterly wage amount. We then multiplied the total quarterly wage amount by four—the number of quarters in a calendar year—to generate an estimate of annual wages for the borrower. Our approach was based on the methodology Education instructs loan servicers to use to calculate annual wages when borrowers provide an alternative to a tax return to document their income on IDR applications. This methodology may understate or overstate income given that borrowers may not have earned the same amount in each of the four quarters. Our estimates of annual wages are based on the wages reported in NDNH for each borrower and do not take into account any pre-tax deductions that may apply when determining IDR payments. Our estimates of annual wages also do not include borrowers’ spousal income or any other taxable income for the borrower that is not included in the NDNH quarterly wage data—such as unemployment compensation received or unearned income such as alimony. We did not independently verify the wages reported in NDNH or the actual total annual income earned by borrowers identified in our match, as this was outside the scope of our review. Using the estimated annual wage, we then determined whether a borrower would have had a monthly payment greater than zero by using Education’s IDR plan repayment calculations for each IDR plan. To calculate the monthly payment, we used (1) the estimated annual wage from our NDNH data analysis; (2) the family size reported on the borrower’s approved IDR plan; (3) the borrower’s approved IDR plan type; and (4) the relevant percentage of the HHS poverty guideline amount for the borrower’s family size, state of residence, IDR plan approval year, and IDR plan type. For borrowers on Income-Based Repayment, New Income-Based Repayment, Pay As You Earn, and Revised Pay As You Earn plans, we rounded all calculated monthly payments that were less than $5 down to zero, in accordance with Education’s repayment calculations. We then identified which borrowers had calculated payments that were greater than zero. We did not determine the actual repayment amount borrowers may have had, as this was outside the scope of our review. Finally, for borrowers for whom we had calculated a payment greater than zero, we determined the total outstanding Direct Loan balance (principal and accrued interest) as of September 2017, based on EDWA data. For our analysis of borrower-reported family sizes, we analyzed the overall distribution of family sizes reported on approximately 5 million approved IDR plans. We reviewed the percentile distribution for family size on all IDR plans in our analysis and identified those in the top 1 percent of the data—in this case, IDR plans that had a reported family size of nine or more. We defined these IDR plans as having atypical family sizes for the Education data. We did not independently verify the family size reported on the IDR plans. For the borrowers with family sizes of nine or more, we determined the total outstanding Direct Loan balance (principal and accrued interest) as of September 2017. To examine the effects of borrowers inaccurately reporting income and family size on loan payment amounts, we analyzed the estimated monthly loan payment amounts for various hypothetical repayment scenarios from Education’s online repayment estimator as of January 2019, which used the 2018 HHS poverty guidelines. To examine the effect of various family sizes on loan payment amounts, we assumed a hypothetical borrower lived in the continental United States; had an adjusted gross income of $40,000; an outstanding Direct Loan balance of $30,000 (close to the average outstanding Direct Loan balance of $33,600 as of September 2018); and an interest rate of 5.1 percent (the Direct Loan 2018-2019 interest rate for an undergraduate borrower). To examine the effect of various incomes on monthly payment amounts, we assumed hypothetical borrowers had adjusted gross incomes based on estimated annual wages common in our data matching analysis ($30,000, $45,000, and $60,000), a family size of one (meaning just the borrower), and lived in the continental United States. For this analysis, we also assumed hypothetical borrowers had an interest rate of 5.1 percent and an outstanding Direct Loan balance of $50,000, which we selected to be high enough to qualify these hypothetical borrowers for all IDR plans at each of the selected income levels. To assess the reliability of the EDWA data, we reviewed documents related to the database and Education loan data generally; interviewed knowledgeable Education officials; performed electronic testing to determine the validity of specific data elements that we used to perform our work; compared the data we received to published Education data on the number of IDR borrowers and amount of their outstanding loans; and compared borrowers’ personal information to the Social Security Administration’s Enumeration Verification System to identify borrowers whose information may not have been accurate. As part of our reliability assessment of the EDWA data, we selected a nongeneralizable sample of 16 borrowers and their IDR plan and loan information from the EDWA data to compare against four selected loan servicers’ records. Specifically, we stratified borrowers into two groups based on common and potentially outlying incomes and family sizes in the EDWA data. We then randomly selected two borrowers from each stratum for each of the four selected loan servicers (a total of four borrowers per loan servicer). We reviewed all IDR plan data in our scope for each selected borrower, including the plan type, family size, income, and total monthly payment. We did not review original documents, such as the IDR applications or documentation of income. We discussed the results of our review with knowledgeable Education and loan servicer officials to gain additional understanding of each selected borrower’s IDR plan information as well as any differences between EDWA and loan servicer data. We originally obtained EDWA data on approximately 6.5 million IDR plans approved between January 1, 2016 and September 30, 2017 that were held by almost 4.8 million Direct Loan borrowers. Based on data reliability issues we identified during our review, we had to limit the scope of our analysis to a subset of EDWA data that we determined were sufficiently reliable for our purposes. Education officials disclosed issues that impacted the IDR plan data reported to Education by one of its loan servicers. Specifically, Education and the loan servicer had identified instances where the loan servicer’s internal data were changed for valid reasons but the changes were not reported to Education correctly. As a result, we excluded data reported by this servicer from all analyses in our report. We also identified issues with monthly payment amounts for some borrowers in the EDWA data. Accordingly, we limited our borrower- reported income analysis to borrowers who reported zero income and had a scheduled monthly payment of zero dollars. Ultimately, we analyzed about 878,500 IDR plans held by about 656,600 borrowers for our income analysis and approximately 5 million IDR plans held by 3.5 million borrowers for our family size analysis. Consequently, our overall income and family size analyses results may be understated and are not generalizable to all IDR plans and borrowers. Consistent with our report scope, our analyses of borrower-reported income focused on identifying indications of potential fraud or error; however, our analyses do not show that fraud or error occurred. It is not possible to determine whether fraud or error occurred through data matching alone. As previously discussed, our estimates of annual wages are based on the NDNH quarterly wage data, and do not take into account any deductions that may be applicable for determining adjusted gross income, which is used to determine IDR plan payment amounts. As a result, our estimates could overstate borrowers’ incomes for IDR plan purposes. Additionally, wages are reported in NDNH quarterly, so we are not able to determine when in a quarter a borrower earned wages. For example, a borrower may have earned wages at the start or end of a quarter, but was not earning wages at the time of submitting the IDR application. Because borrowers are only required to certify their income annually, such a scenario would not constitute fraud or error even though it would result in a match in our analysis. In addition, our use of Education’s methodology to annualize wages based on quarterly wages may understate or overstate income if a borrower did not earn wages at the same level over the entire year. We are also not able to identify additional taxable income that is not reported to NDNH but should have been included on borrowers’ IDR applications, which could understate borrowers’ incomes. Consequently, our analysis may overstate or understate the number of borrowers who reported no income on their IDR application yet may have had sufficient wages to warrant a monthly student loan payment. To assess the reliability of the NDNH data, we reviewed documents related to the database, interviewed knowledgeable HHS officials, and performed electronic testing to determine the validity of specific data elements in the NDNH data that we used to perform our work. On the basis of our own reliability assessment results, we determined that the NDNH data were sufficiently reliable for the purposes of this report. We conducted this performance audit from June 2017 to June 2019, 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 obtained provides a reasonable basis for our findings and conclusions based on our audit objectives. Appendix II: Comments from the U.S. Department of Education Appendix III: GAO Contacts and Staff Acknowledgments GAO Contacts Staff Acknowledgments In addition to the contacts named above, Debra Prescott and Philip Reiff (Assistant Directors), Nancy Cosentino and Mariana Calderón (Analysts- in-Charge), Sarah Cornetto, Jeffrey G. Miller, and Rachel Stoiko made key contributions to this report. Additional assistance was provided by Susan Aschoff, David Ballard, Deborah Bland, Benjamin Bolitzer, Melinda Cordero, Vijay D’Souza, Kevin Daly, Angie Jacobs, Candace Silva-Martin, Sheila R. McCoy, Maria McMullen, Kevin Metcalfe, John Mingus, Drew Nelson, Mimi Nguyen, Matt Valenta, and Ariel Vega.