A two‑stage hybrid framework for default prediction in digital lending through integrating internal and external credit models.
With the rapid growth of online lending platforms, credit risk management has become increasingly important. Aligned with Basel Committee recommendations, this study proposes a two-stage hybrid framework that integrates internal default prediction models with external credit ratings at the decision-making level. Unlike prior studies that either rely solely on internal models or treat external ratings as input features, the proposed framework preserves the distinct strengths of both sources. In the first stage, twelve machine learning models are combined with five data balancing techniques and feature selection, yielding 60 distinct configurations evaluated under class imbalance. Performance is assessed using conventional metrics (F1, G-mean, AUC) and a profit-based metric (Profit_Score) that reflects the economic impact of model decisions by quantifying avoided losses and forgone revenues. Logistic regression with random oversampling is selected as the optimal model. The key methodological contribution lies in the second stage, where a dynamic credit rating adjustment mechanism is introduced based on a composite score integrating predicted default probability, external credit rating, and loan amount. Results show that the dynamic approach outperforms both the static strategy (by 14.55%) and the standalone internal model (by 30.4%). The findings demonstrate that decision-level integration of internal and external models, and addressing class imbalance, enhances both predictive performance and profitability.