Machine Learning Models Evaluation and Feature Importance Analysis on NPL Dataset
Predicting the probability of non-performing loans for individuals has a
vital and beneficial role for banks to decrease credit risk and make the right
decisions before giving the loan. The trend to make these decisions are based
on credit study and in accordance with generally accepted standards, loan
payment history, and demographic data of the clients. In this work, we evaluate
how different Machine learning models such as Random Forest, Decision tree,
KNN, SVM, and XGBoost perform on the dataset provided by a private bank in
Ethiopia. Further, motivated by this evaluation we explore different feature
selection methods to state the important features for the bank. Our findings
show that XGBoost achieves the highest F1 score on the KMeans SMOTE
over-sampled data. We also found that the most important features are the age
of the applicant, years of employment, and total income of the applicant rather
than collateral-related features in evaluating credit risk.