Bin Luo, Qi Zhang, and Somya D Mohanty.
In: Proceedings of the 2018 International Conference on Data Science ICDATA’18, 2018 World Congress in Computer Science, Computer Engineering, & Applied Computing (June 2018).
Publication year: 2018

Student loans occupy a significant portion of the federal budget, as well as, the largest financial burden in terms of debt for graduates. This paper explores data-driven approaches towards understanding the repayment of such loans. Using statistical and machine learning models on the College Scorecard Data, this research focuses on extracting and identifying key factors affecting the repayment of a student loan. The specific factors can be used to develop models which provide predictive capability towards repayment rate, detect irregularities/non-repayment, and help understand the intricacies of student loans.