Application of Binary Logistic Regression to Identify Determinants of Non-Performing Loans

Abstract

This study aimed to identify the factors associated with non-performing loans (NPLs) in banks and develop a borrower-level model to estimate the probability of loan default. Unlike previous studies that mainly focus on bank-level financial indicators or macroeconomic factors, this study utilizes borrower characteristics and loan information obtained from credit application data at a commercial bank in Palembang, Indonesia. The study used data from 100 borrowers, with predictor variables including age, number of family dependents, total household income, occupation, educational attainment, loan amount, loan term, and monthly installment amount. Binary logistic regression with backward elimination was applied to identify significant predictors of NPLs and to estimate the probability of loan default. The results showed that occupation, loan amount, and loan term significantly influenced the occurrence of non-performing loans. The final model achieved a classification accuracy of 81% and an area under the receiver operating characteristic curve (AUC) of 0.858, indicating good predictive performance. The obtained binary logistic regression model can be used to estimate the probability of non-performing loans and assist banks in identifying potential credit risks during the credit evaluation process.