Machine Learning Model for Predicting the Repayment Rate of Loan Takers

Sammanfattning: Machine Learning (ML) uses statistics to find patterns in high dimensional data. The Swedish Board of Student Finance (CSN) wants to improve the way they classify new loan takers. Using Machine Learning (ML) on data from previous loan takers can establish patterns to use on new loan takers. The aim of this study is to investigate if CSN can improve the way they classify loan takers by their ability to pay back their loan. In this study, different ML models are applied to a data set from CSN, their performance are compared and investigated by the most related factors affecting an individuals repayment rate. A data set of a total of 2032095 individuals were analysed and used in the different models. Using Random Forest (RF) for binary classification produced the best result with a sensitivity of 0.9695 and a specificity of 0.8058.