Credit risk management is critical in developing economies where high default rates threaten financial stability. This study compares traditional logistic regression (TLR) and Bayesian logistic regression (BLR) for predicting loan default using anonymized National Credit Regulator (NCR) data from 5000 South African loan borrowers (2018–2022). The NCR data included both bank and non-bank lenders. The findings indicate that the BLR model outperformed TLR, achieving an average precision of 0.94. Loan terms, inflation rates, and income bands of R5000–R10,000 and R20,000–R50,000 were associated with higher default risk, whereas higher credit scores and personal loan products were associated with lower default risk. Model performance improved when focusing on these predictors rather than all variables. Using a 0.5 probability threshold, BLR classified 94.5% of borrowers as high risk. Findings highlight the practical value of BLR for identifying key predictors and improving borrower risk classification. These insights can inform targeted strategies such as enhanced screening for long-term loans, monitoring during inflationary periods, and tailored repayment plans for vulnerable income groups, supporting responsible lending and portfolio stability.
Masekoameng et al. (Fri,) studied this question.