When it comes to the risk of students facing money management and loan repayment difficulties, less is definitely more. But reducing student risk requires increasing institutional knowledge.
Sometimes, that starts with knowing what you don’t know – and there can be a lot of unknowns. However, that’s good news, because that discovery process is a great opportunity to be more effective and proactive about helping your students repay their loans and your institution minimize its default rate.
More good news: Valuable information is hiding in plain sight. There is a wealth of borrower data from Federal Direct Loan servicers, nonprofit servicers and the National Student Loan Data System (NSLDS). By studying this data and the patterns they reveal, you can gain powerful insight into who is becoming delinquent or defaulting and why. You can use that insight to take data-driven actions. This makes your default management process more proactive, effective and efficient. For example, you can identify and measure correlations between loan amounts and repayment trends. Other areas that are often factors in loan repayment outcomes include credit hours completed, initial repayment habits and when students withdraw.
This is critical information because a common mistake in a default prevention strategy is taking a blanket approach to contacting and counseling borrowers. In such cases, you may be committing too many resources to borrowers who are probably going to repay their loans without intervention. On the other hand, you may not be offering enough to those who are most likely to struggle with student loan repayment. Institutions that use data to inform their outreach can prioritize borrowers by default risk level. This allows them to tailor resources and messaging where they will have the greatest impact.
But many institutions lack the resources needed to comb through data and maintain an effective level of analysis. A key part of our nonprofit mission is to lessen that burden. Over the past several decades of work with millions of students and thousands of campuses, we’ve developed algorithms that help remove the complexity of identifying and accurately analyzing borrower data.
We begin the process with an institutional default risk analysis. This includes calculating a risk score for each borrower. A risk analysis reveals immediate opportunities to become more efficient with outreach by targeting the right borrowers with the right message. Following that, our patented loan management system continuously updates borrower data. Using the latest information and real-time reporting, it identifies default characteristics and the high-risk attributes specific to the unique borrower populations of the institution. This ensures that borrower communication is continuously improved and tailored to the latest risk score of each borrower.
By using one platform to aggregate borrower data and perform both historic and predictive analyses, institutions can easily monitor detailed repayment trends. At the same time, they are maintaining visibility of the broader picture. They can compare cohort default rates against national, state and peer institution averages. They can also identify repayment success and default characteristics unique to their borrowers. And, they can receive the latest projections for all active cohorts, including best- and worst-case cohort default rates.
If you’d like to try a data-informed approach to helping your students repay their loans – without the headache of data analysis – consider getting a free and secure risk analysis of your school portfolio report.
Kim Fish is director of product innovation for Student Connections.