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.
One of the first efforts to apply methods on a standardized dataset was in the research conducted by Kelchen and Li (2017) @cite_16 , by using the College Scorecard data. In their study, the dataset was used to test associations between institutional characteristics and one year loan repayment rates. The study also aimed to analyze the effects on the repayment based ond the number of years after a student has left college. The goal of the study is to develop multiple linear regressions capable of analyzing repayment using predictor variables. While the study choose a fixed list of features (based on prior research) to be their predictor variables, a better model of establishing such variables would be to conduct an exhaustive search of all available features in the dataset. The research presented in the paper aims to identify such features using a multi-model approach, where the models are able to suggest the best performing predictors in the dataset.
@cite_16
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The federal government holds colleges accountable if too many of their students default on loan repayment, but the default measure traditionally used captures only a fraction of students who are struggling to repay their loans. The 2015 College Scorecard dataset introduced a new loan repayment metric, showing that the percentage of students who have not reduced the principal balance of their loans by at least $1 over a given period of time far outstrips the traditional loan default measure. Using a sample of 3,595 colleges, we test the extent to which student demographics, institutional characteristics, and state-level economic factors are associated with repayment rates and default rates. We also examine whether factors associated with loan repayment rates change between one and seven years after students begin repayment. We find that characteristics traditionally associated with economic disadvantage, including being a first-generation college student or a member of an underrepresented minority group, t... One of the aims of credit scoring models is to predict the probability of repayment of any applicant and yet such models are usually parameterised using a sample of accepted applicants only. This may lead to biased estimates of the parameters. In this paper we examine two issues. First, we compare the classification accuracy of a model based only on accepted applicants, relative to one based on a sample of all applicants. We find only a minimal difference, given the cutoff scores for the old model used by the data supplier. Using a simulated model we examine the predictive performance of models estimated from bands of applicants, ranked by predicted creditworthiness. We find that the lower the risk band of the training sample, the less accurate the predictions for all applicants. We also find that the lower the risk band of the training sample, the greater the overestimate of the true performance of the model, when tested on a sample of applicants within the same risk band — as a financial institution would do. The overestimation may be very large. Second, we examine the predictive accuracy of a bivariate probit model with selection (BVP). This parameterises the accept–reject model allowing for (unknown) omitted variables to be correlated with those of the original good–bad model. The BVP model may improve accuracy if the loan officer has overridden a scoring rule. We find that a small improvement when using the BVP model is sometimes possible. We hypothesized that college major persistence would be predicted by first-year academic performance and an interest-major composite score that is derived from a student’s entering major and two work task scores. Using a large data set representing 25 four-year institutions and nearly 50,000 students, we randomly split the sample into an estimation sample and a validation sample. Using the estimation sample, we found major-specific coefficients corresponding to the two work task scores that optimized the prediction of major persistence. Then, we applied the estimated coefficients to the validation sample to form an interest-major composite score representing the likelihood of persisting in entering major. Using the validation sample, we then tested a theoretical model for major persistence that incorporated academic preparation, the interest-major composite score, and first-year academic performance. The results suggest that (1) interest-major fit and first-year academic performance work to independently predict whether a student will stay in their entering major and (2) the relative importance of two work task scores in predicting major persistence depends on the entering major. The results support Holland’s theory of person-environment fit and suggest that academic performance and interest-major fit are key constructs for understanding major persistence behavior. In this paper, we set out to compare several techniques that can be used in the analysis of imbalanced credit scoring data sets. In a credit scoring context, imbalanced data sets frequently occur as the number of defaulting loans in a portfolio is usually much lower than the number of observations that do not default. As well as using traditional classification techniques such as logistic regression, neural networks and decision trees, this paper will also explore the suitability of gradient boosting, least square support vector machines and random forests for loan default prediction.Five real-world credit scoring data sets are used to build classifiers and test their performance. In our experiments, we progressively increase class imbalance in each of these data sets by randomly under-sampling the minority class of defaulters, so as to identify to what extent the predictive power of the respective techniques is adversely affected. The performance criterion chosen to measure this effect is the area under the receiver operating characteristic curve (AUC); Friedman's statistic and Nemenyi post hoc tests are used to test for significance of AUC differences between techniques.The results from this empirical study indicate that the random forest and gradient boosting classifiers perform very well in a credit scoring context and are able to cope comparatively well with pronounced class imbalances in these data sets. We also found that, when faced with a large class imbalance, the C4.5 decision tree algorithm, quadratic discriminant analysis and k-nearest neighbours perform significantly worse than the best performing classifiers.