By Angela (Ceresnie) Prince, former Climb CEO
The topic of student lending was popular during the 2020 election cycle — and this provides an opportunity to share forward-leaning ideas on how to improve overall educational opportunities and outcomes. Responsible innovations in student lending and related underwriting can increase education access, reduce bias, and drive positive economic outcomes.
The status quo for consumer credit underwriting, including for education financing, largely relies on a set of long-established metrics such as credit bureau data, income, and the most widely adopted credit-scoring model, FICO. However, these metrics alone are not the best we can do when it comes to reducing bias in consumer lending and expanding opportunities for individuals to invest in their futures through career-advancing education. Innovative approaches and underwriting models will be the solution, but they must be responsible and incremental in order to avoid unintended outcomes.
In the context of financing for professional and skills-training programs, the traditional consumer lending model outlined above fails to account for the future economic benefits derived from the education investment. The model is static in that it captures the individual’s creditworthiness at the current point in time, when they may be in a low-paying job or not employed at all.
Data from Climb learners show that graduates of well-vetted education and training programs can experience median salary increases of over 70%. This underscores how traditional underwriting approaches that would deny a student credit for attending such programs are both unfair and unnecessarily restrictive.
Innovative approaches to this category of consumer lending hold substantial promise in expanding access, promoting fairness, and driving economic empowerment. But the innovation must be responsible and evolutionary in its approach in order to avoid advancing new forms of bias and discrimination.
One particular area that holds substantial promise in expanding access and fairness is considering the expected future income of a graduate from a well-regarded professional or skills-training program. Much like a lender to a small business will consider the expected growth of a new venture, a lender should consider whether a particular professional or vocational education program is likely to drive a higher income for the student upon graduation. This approach protects the student by ensuring the expected future income justifies the educational investment and by providing capital access when a traditional approach would likely result in a decline.
Take this scenario2:
In this example, a lender using John’s current income would likely deny him the loan and block his path to gaining skills training. At Climb, we know that this one-month truck driving program is likely to result in a salary increase for John — getting him far closer to that $40,000 range. So we consider that salary when reviewing John’s DTI. And in our version of this scenario, John is empowered to attend the program and get his Commercial Drivers License.
Indeed, consideration of a debt-to-future-income ratio based on the outcomes and track record of educational programs can provide currently underemployed and unemployed prospective students (or those who would not otherwise have financing) with the ability to invest in additional education and training. This approach is aligned with student interests by helping them pursue educational programs that truly expand economic opportunity and professional advancement, while not saddling them with debt and illusory benefits.
The consideration of expected future income when analyzing an ability to repay is an incremental credit innovation, is consistent with existing law, and helps generate additional career outcomes data that can increase transparency regarding school performance and better inform student decisions.
With respect to the incremental and compliant nature of this approach, as noted above, income information — both current and future — is not only a well-accepted underwriting metric, but is critical in determining a borrower’s ability to repay. Considering future income ensures that the debt burden incurred by the student is worth the expected return. It also prevents a student from unfairly being denied credit for pursuing an educational program that can drive economic and career advancement.
It is important for responsible lenders in this space to collect, review, and confirm reported graduation rates, job placement rates, and expected salaries. Over time and through the dissemination of outcomes data, students will be in a better position to evaluate the merits of particular professional and skills-training tracks and programs. Transparency of this type can help eliminate programs that over-promise and under-deliver in terms of student outcomes.
Going forward we can do better than the status quo when it comes to fairness, access, and accuracy in consumer lending. Technological advances (a topic that merits its own separate analysis), including with respect to machine learning and quantitative analysis, hold substantial promise in improving lending outcomes across these factors. And as explained here, so does measuring real-world data — especially when it comes to economic outcomes from professional and skills-training programs — in order to better understand expected future incomes. This incremental approach unlocks access and opportunity in ways that status quo approaches simply cannot.
1 – Based on nearly 7,000 survey responses. The data points include Climb’s all-time survey respondents, with duplicate responders removed, that both: 1) reported to have graduated from their program and 2) reported both pre-program and post-program income data.
2 – Assumptions in the example included in this article:
- John’s gross salary is $24,960, if he works 40 hours a week for 52 weeks out of the year.
- John’s monthly student loan payment for the CDL program is going to be approximately $187/month (assuming a 6.99% interest rate and 36 month term).