Taehyoung Kwon  Eungseon An  Doguk Kim

10.5626/JOK.2025.52.8.660

credit default prediction Data Imbalance time-series classification data augmentation

Credit default cases are considerably rarer than non-default cases, leading to a significant class imbalance issue. This imbalance negatively impacts the performance of predictive models. To tackle this problem, this study introduces T-SMOTE, a time-series-based data augmentation technique. Unlike traditional SMOTE, T-SMOTE leverages the continuity of time-series data to generate samples that are closer to the boundaries, thereby enhancing model performance. However, the original T-SMOTE had a limitation in handling short time-series data, which was addressed by incorporating the Zero-Padding technique. Comparative experiments using data from American Express showed that T-SMOTE effectively mitigates the data imbalance problem. These findings suggest that advanced data augmentation technologies can create new opportunities for credit risk management in the financial industry.