Consumer loans’ first payment default detection: a predictive model

Abstract

A default loan (also called nonperforming loan) occurs when there is a failure to meet bank conditions andrepayment cannot be made in accordance with the terms of the loan which has reached its maturity. In this study,we provide a predictive analysis of the consumer behavior concerning a loan’s first payment default (FPD) using a realdataset of consumer loans with approximately 600,000 records from a bank. We use logistic regression, naive Bayes,support vector machine, and random forest on oversampled and undersampled data to build eight different models topredict FPD loans. A two-class random forest using undersampling yielded more than 86% on all performance measures:accuracy, precision, recall, and F1-score. The corresponding scores are even as high as 96% for oversampling. However,when tested on the real and balanced dataset, the performance of oversampling deteriorates as generating syntheticdata for an extremely imbalanced dataset harms the training procedure of the algorithms. The study also provides anunderstanding of the reasons for nonperforming loans and helps to manage credit risks more consciously.