Predictive Analytics

How real-time decisioning is shaping higher education.

GUEST COLUMN | by Joe DeCosmo and Sean Naismith

CREDIT Enova.pngThe need for institutions of higher education to deliver solid return-on-investment for their students has never been greater. With the cost of traditional higher education continuing to rise, universities are in competition with each other to essentially secure high-paying careers for students upon graduation. Meanwhile, for-profit universities are striving to meet gainful employment regulations and debt-to-earnings requirements that are meant to ensure students graduate with the skills and tools necessary for career success as well as the financial capability to repay student loan debt with sufficient career earnings.

Now, and in the future, higher education institutions need innovative ways to deliver on their students’ return on investment.

Now, and in the future, higher education institutions need innovative ways to deliver on their students’ return on investment. Advanced, real-time predictive analytics decisioning technology is emerging as a viable solution for schools to meet regulated or self-set, debt-to-earnings goals for potential students.

While there are many ways in which schools can hope to improve their student debt-to-earnings ratio — for instance, investing in career centers or student financial advisement — ­real-time, predictive analytics decisioning technology can deliver results that are truly measurable by predicting debt-to-earnings before a student has even set foot in the classroom. Indeed, ensuring that students can meet that necessary financial threshold after graduating begins with the admissions process. Through unbiased and data-driven decisions enabled by real-time technology, universities can make an informed decision about student acceptance based on predicted future financial viability.

Real-time decisioning technology harnesses a wide array of data to forecast potential trouble spots. Examples of this data include:

  • Interest rates of loans currently held
  • Program and eventual degree earned
  • Geography
  • Credit data
  • Macro-economic data

These are just a few of the variables. Powerful analytics engines can analyze any combination of factors upon application submission in real time to determine the likelihood of gainful employment at both the micro- and macro-levels, helping schools to maximize their education costs with student earning potentials. It’s a strategic advantage for the school to make its programs attractive to students and GE-compliant, and it’s a boon for the students that are accepted and can go on to achieve a financially solvent livelihood.

Schools looking to take advantage of real-time decisioning technology have a few options when it comes to implementing the solution. One option is to build the technology with a team of data scientists and technologists. The time (often years), talent resources, and money needed to build such an analytics engine constitutes a substantial investment and is one without a guarantee of success. The financing alone would be a tremendous investment, even for the most prestigious and successful schools.

Another option is to outsource the decisioning to an analytics decisioning company capable of accessing and analyzing thousands of third-party data sources while integrating with a school’s existing learning management systems. These analytics processors are designed to handle multiple inputs, and they can analyze annual loan payments, annual earnings, discretionary income rates and other figures needed to forecast accurate debt-to-earnings rates.

As with many decisions, the best answer often lies somewhere in the middle. In this case, the middle ground would be outsourcing the real-time decisioning technology capabilities while retaining a very small team to handle modeling.

In the current academic and regulatory environment, the need for real-time decisioning technology is greatest for U.S. for-profit colleges who must meet specific GE requirements. Should a school fail to meet them, they risk major ramifications, including the withdrawal of federal financial aid. As you can imagine, the loss of such funding would be a momentous loss for any school and could even lead to closure — as we’ve seen in recent news.

However, with tuition rates still on the rise and myriad school options for prospective students to choose from, I believe even traditional higher education institutions must craft programs that are attractive and provide a satisfactory ROI for potential students. Advanced, real-time analytics decisioning technology can provide schools with the insights they need to meet students’ expectations for earnings upon graduation.

Joe DeCosmo is Chief Analytics Officer and Sean Naismith is Head of Analytics Services at Enova Decisions.


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