Monday, January 4, 2016

Putting ‘Big Data’ to Work to Prevent Student Loan Default
Submitted by: Angela Henry, USA Funds Account Executive

Which borrowers at your school are most likely to default on their student loans?

It’s a question you need to be able to answer to most effectively prevent those defaults. And there’s only one way to know the answer:

Analyze the data.

The importance of ‘big data’
Being proactive in your default prevention means not only helping borrowers who are in trouble to get back on track, but also keeping borrowers whose loans are in good standing from falling behind in the first place.

Seems like a daunting — and expensive — task. And it can be, if you take the blanket approach traditionally employed by schools working to lower their cohort default rates.

But the better approach is to work smarter, not harder.

Determine the characteristics of your institution’s borrowers who are most likely to default, and then take a targeted approach to default prevention.

To find those characteristics, you have to go beyond making assumptions. That’s because there’s no set rule for who most frequently defaults. The attributes of defaulters at one school are not necessarily the same as those at another.

You have to analyze the data for your own borrowers.

Then you can target your default prevention, allocating the most support to those who are likely to need the most help. This approach allows you to make the best use of your resources.

What data can you study to determine which borrowers are at greatest risk of defaulting? Here are some examples:

·         Standardized test scores.
·         Student application details.
·         Contact or interaction history.
·         GPA.
·         Full- or part-time or online enrollment status.
·         Major.
·         Employment status.
·         Involvement in on-campus activities
·         Student loan and grant information.
·         Alumni engagement.

Putting ‘big data’ to work
Institutional data, servicer files, and the National Student Loan Data System all are good sources of borrower information that can help you find out who’s most at risk of defaulting at your school.

You can turn that data into actionable insights that guide your targeted borrower outreach plan. USA Funds’ cohort analysis approach is to categorize your portfolio of borrowers into three levels of default risk: low, moderate and high. And that default risk, along with a borrower’s repayment status and your school’s default prevention budget, should dictate how you implement your borrower outreach strategies.


If you need assistance with default prevention planning, visit www.borrowerconnect.org

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