Classification of retail loans using decision tree

Abstract

While there is extensive literature on the prediction of corporate bankruptcies, there is little literature on the classification of retail borrowers. There are several ways to analyse the data, which may yield different results. In this paper, my aim is to predict the default of household loans using decision tree.  I found one significant explanatory variable, which was the ratio of the repayment to the contract amount. For my analysis I used two samples with different compositions. Both have high classification accuracy. Overall, the second model is better, with a classification accuracy of 84,4%.