Predicting BNPL Loan Defaults: A Comparison of Ensemble Learning Models Combined With Balancing Techniques and an Analysis of the Impact of Digital Literacy

ABSTRACT The rapid growth of e‐commerce and the emergence of BNPL (Buy Now, Pay Later) financial products have significantly increased loan applications. However, the accumulation of losses from customer defaults poses a serious bankruptcy risk for BNPL providers. Unlike most studies that focus on credit scoring for traditional microloans, this research specifically uses BNPL loan data. A major concern in this domain is the imbalanced nature of the data, which can adversely affect model performance. To this end, we compared ensemble learning models in combination with data balancing methods and proposed a novel combination of logistic regression, SMOTE‐NC, and LightGBM, which has not been extensively explored in previous studies. Additionally, we introduced two new variables— ‘active internet banking’ and ‘active mobile banking’—to investigate whether the use of digital banking platforms can indicate creditworthiness. Regression analysis confirmed the significance of the new variables, alongside key predictors such as ‘Education’, ‘Collateral type’, ‘Long‐term accounts count’, ‘Received loans count’, ‘Active loans count’, and ‘Loan amount’. The proposed method achieved an F1‐score of 84.66% for the default class, a 23% improvement over models without balancing techniques. Implementing this model could reduce realized BNPL losses by 26.84%, underscoring its potential to mitigate risks in this sector.