Default on a credit prediction using decision tree and ensemble learning techniques

Abstract

Due to the high importance of credit risk management and the prediction of the default on a credit loan in banks in recent years in most developed countries. This research experimented with three machine learning algorithms which are decision tree and ensemble techniques based on a decision tree-like random forest which is bootstrap aggregation machine learning model and adaptive boosting is a boosting machine learning model. All models were used to perform a binary classification to classify the clients into trusted clients who would pay back the debts who are creditable and a none trusted clients who won’t pay the debts In the time which are who are not creditable. All the models gave nearly the same results based on the selected performance measures which are precision, recall, and f1 measure. There was no significant change when they performed on a real class imbalanced dataset of the default credit card clients of Taiwan.