Seto J. Bagdoyan
Workplace of Public Affairs
To help relieve the responsibility of federal student education loans, borrowers can put on for Income-Driven Repayment plans. The plans use borrowers’ taxable income and household size to ascertain a payment rate that is affordable. Monthly obligations is often as low as $0 but still count toward prospective loan forgiveness following the payment duration.
Our suggestions are when it comes to Department of Education to accomplish more to confirm borrowers’ family and income size as a result of possible mistake or fraudulence:
Significantly more than 76,000 borrowers making no payments that are monthly have had enough earnings to pay for one thing
Significantly more than 35,000 borrowers had authorized plans with atypical household sizes of 9 or even more
How household size impacts re payment quantities in a few Income-Driven Repayment plans for the debtor with $40,000 in taxable earnings
Graphic showing that a borrower that is single re re payment is $182 but decreases to $74 with a household of 3 and $0 with a family group of 5
Seto J. Bagdoyan
Workplace of Public Affairs
GAO identified indicators of possible fraudulence or mistake in earnings and household size information for borrowers with authorized Income-Driven Repayment (IDR) plans. IDR plans base monthly obligations on a borrower’s earnings and household size, expand repayment durations through the standard ten years to as much as 25 years, and forgive staying balances at the conclusion of the period.
Zero earnings. About 95,100 IDR plans were held by borrowers whom reported zero earnings yet potentially earned sufficient wages to produce month-to-month education loan re re payments. This analysis will be based upon wage information through the nationwide Directory of brand new Hires (NDNH), a dataset that is federal contains quarterly wage information for newly employed and current employees. Relating to GAO’s analysis, 34 % of those plans had been held by borrowers who’d approximated yearly wages of $45,000 or maybe more, including some with predicted yearly wages of $100,000 or higher. Borrowers with one of these 95,100 IDR plans owed almost $4 billion in outstanding loans that are direct of September 2017.
Family size. About 40,900 IDR plans were authorized centered on household sizes of nine or higher, that have been atypical for IDR plans. Almost 1,200 of those 40,900 plans had been approved predicated on household sizes of 16 or higher, including two plans for various borrowers which were authorized using household measurements of 93. Borrowers with atypical family members sizes of nine or even more owed nearly $2.1 billion in outstanding Direct Loans as of September 2017.
These results suggest some borrowers may erroneously have misrepresented or reported their earnings or family members size. Each year and potentially increasing the ultimate cost of loan forgiveness because income and family size are used to determine IDR monthly payments, fraud or errors in this information can result in the Department of Education (Education) losing thousands of dollars of loan repayments per borrower. Where appropriate, GAO is referring these leads to Education for further investigation.
Weaknesses in Education’s procedures to confirm borrowers’ earnings and household size information limitation being able to detect potential fraudulence or mistake in IDR plans. While borrowers obtaining IDR plans must definitely provide evidence of taxable earnings, such as for example taxation statements or spend stubs, Education generally accepts borrower reports of zero earnings and borrower reports of household size without verifying the info. Although Education doesn’t now have usage of federal sourced elements of information to validate debtor reports of zero earnings, the division could pursue such access or get private information sources for this specific purpose. In addition, Education hasn’t methodically implemented other data analytic methods, such as for example making use of information it currently needs to identify anomalies in earnings and household size that could suggest fraud that is potential mistake. Although data matching and analytic methods might not be enough to identify fraudulence or mistake, combining these with follow-up procedures to confirm information about IDR applications may help Education lessen the threat of making use of fraudulent or erroneous information to determine month-to-month loan re re payments, and better protect the federal investment in figuratively speaking Homepage.
As of 2018, almost half of the $859 billion in outstanding federal Direct Loans was being repaid by borrowers using IDR plans september. Prior GAO work discovered that while these plans may relieve the duty of education loan financial obligation, they could carry high prices for the government that is federal.
This report examines (1) whether you can find indicators of possible fraud or mistake in earnings and household size information supplied by borrowers on IDR plans and (2) the level to which Education verifies these records. GAO obtained Education information on borrowers with IDR plans authorized from January 1, 2016 through September 30, 2017, the most up-to-date data available, and evaluated the danger for fraudulence or mistake in IDR plans for Direct Loans by (1) matching Education IDR plan data for a subset of borrowers whom reported zero income with wage information from NDNH for the exact same time frame and (2) analyzing Education IDR plan information on borrowers’ family members sizes. In addition, GAO reviewed appropriate IDR policies and procedures from Education and interviewed officials from Education.
GAO advises that Education (1) obtain information to confirm earnings information for borrowers whom report zero earnings on IDR plan applications, (2) implement information analytic methods and follow-up procedures to validate debtor reports of zero income, and (3) implement data analytic techniques and follow-up procedures to validate borrowers’ family members size. Education generally consented with your suggestions.