Wang, Y. and Priestley, J.L. (2017) Binary Classification on Past Due of Service Accounts Using Logistic Regression and Decision Tree. Grey Literature from PhD Candidates.
http://digitalcommons.kennesaw.edu/dataphdgreylit/

has been cited by the following article:

• TITLE: Machine Learning Approaches to Predict Loan Default

  AUTHORS: Wanjun Wu

  KEYWORDS: Machine Learning, Random Forest, Loan Default, Prediction Model

  JOURNAL NAME: Intelligent Information Management, Vol.14 No.5, September 28, 2022

  ABSTRACT: Loan lending plays an important role in our everyday life and powerfully promotes the growth of consumption and the economy. Loan default has been unavoidable, which carries a great risk and may even end up in a financial crisis. Therefore, it is particularly important to identify whether a candidate is eligible for receiving a loan. In this paper, we apply Random Forest and XGBoost algorithms to train the prediction model and compare their performance in prediction accuracy. In the feature engineering part, we use the variance threshold method and Variance Inflation Factor method to filter out unimportant features, and then we input those selected features into Random Forest and XGBoost models. It turns out that Random Forest and XGBoost show little difference in the accuracy of their predictions since both get high accuracy of around 0.9 in the loan default cases.