In the wake of the last financial crisis, major banks retreated from lending to learners who hoped to attend trade schools or earn non-degree credentials.
As a credit officer of education finance at Chase Bank, I watched in frustration as loans to qualified people in good training programs that we would have made before the collapse were suddenly out of the question. Increased regulatory and financial pressure scared banks away.
As the recovery revealed a greater need for nontraditional training and upskilling programs, learners were left with few funding options to pursue this increasingly important kind of education. Millions of unemployed or underemployed adults were newly-interested in trade schools and other skill-based programs as a route back into the workforce.
Without the involvement of major banks, these skills-seekers were often charged exorbitant fees and interest by fringe financial entities to take on the risk that bigger banks were no longer willing to assume. The risk was shifted to the student. Predictably, this had a devastating effect on low-income workers, contributing to rising loan default rates and financial distress.
As we work to recover from the economic fallout of COVID-19, we can’t afford to repeat these mistakes.
A massive number of American workers have been sidelined by the pandemic, with unemployment sitting at nearly 7% (and underemployment likely at some multiple of this figure). People will need to retool to get back to work.
The vast majority of these displaced and under-utilized workers say that they are interested in non-degree programs and other forms of short-term training as a means of getting their careers back on track. But skill-seekers and students shouldn’t have to assume outsized risk to facilitate this training; instead, we can chart a better way by underwriting industries and programs, not just individual learners.
Lending for education in general—and specifically for technical and skills-based training programs—has nested risks that do not typically exist in other asset classes. When making a car loan, for example, the risk assessment process includes examining credit and employment of the consumer to determine their current ability to repay, with payments beginning right away. With educational lending, lenders have to assess the likelihood of completing the course of study and the likelihood of finding employment with sufficient income—only then can they determine a student’s ability to repay the loan.
Examining credit at the time of enrollment does very little to assess the first two of these educational lending risks. To be even more frank, the credit standards in place today are a mechanism by which lenders can require prime and super-prime co-signers. In fact, more than 90% of private educational loans are cosigned by parents, grandparents or other family members—a percentage that has not changed over the past 10+ years.
LendEDU recently published several metrics that show this dynamic in detail: 1) the average credit score of a private student loan applicant is 638, the average score of approved applications is 748, and 2) the approval rates for applications with cosigners is 36% while the approval rates for applications without cosigners is 9%. In an honest intellectual accounting, we would label this asset class family loans, not student loans.
Through this approach, access to training for highly-qualified individuals from low socioeconomic backgrounds is choked-off by the lack of credit-qualified cosigners, exacerbating the inability for these folks who need it the most to access career and salary advancement.
The key is to redesign our systems for understanding and valuing risk.
Most of the prevailing financial industry practices use backward-looking measures of individual behavior like credit scores, and don’t give either lenders or borrowers an accurate picture of the risk of education investments.
Unlike in 2008, more precise measures are now available that allow us to fund training based on program quality, labor market demand, and an individual’s likelihood of success in a program, as established by prior educational, military, and work experiences.
Evolving data systems allow us to better assess the outcomes and return on investment of individual education and training programs. Meritize, for example, quantitatively assesses an individual’s track record of completing objectives as part of its funding decisions. This methodology has resulted in completion rates for a portfolio of students that are 15% higher than those of their peers.
New forms of labor market data—powered by the digitization of hiring transactions—can help us better understand regional demand for talent and even how many people from particular training programs get jobs. Platforms such as Monster.com and Burning Glass provide data at an unprecedented level of detail, allowing market participants to better understand demand, pay scale, and other important factors.
There is also growing recognition among employers that participation in talent creation is more efficient than searching for and retaining already existing talent. They are actively working to improve the connections between education and work by more clearly articulating the skills they need for specific roles. Some, too, are stepping up as “co-signers” on education by either paying for training directly or guaranteeing a hiring outcome if learners successfully complete high-quality, vetted programs.
With 20 million Americans looking to get back to work, the need is urgent. They require not just greater awareness of options, but access to financial capital on fairer terms to help them find the right pathway. The country now has the tools to rethink how we underwrite education and training, but leaders in finance, philanthropy, and the government must be willing to try a new way. When this new wave of displaced workers turns to education, they can’t be met with a closed door.