Credit Risk Assessment: A Comparative Study of Feature Selection and Oversampling Techniques on High-Dimensional Imbalanced Datasets

Credit risk assessment is an essential task for financial institutions, as it evaluates the likelihood of loan defaults. Misclassifying loan applications can result in substantial economic losses. However, accurately predicting customers' creditworthiness presents challenges for machine learning models due to high dimensionality and imbalanced class distributions, which can severely impact overall model performance. This implements three feature selection (FS) techniques, such as Random Forest with Highly Correlated features (RFHC), Information Gain, and Chi-Square to address the high dimensional problem. Additionally, SMOTE is implemented to address the imbalanced dataset. The model performance is analyzed using three different approaches. First, employing only feature selection techniques, in the second approach, we employed feature selection followed by oversampling, and in the final approach, oversampling prior to feature selection is employed. The experimental results indicate that implementing feature selection followed by oversampling gives better performance compared to the other two approaches. Among the feature selection techniques, RFHC outperforms the others, achieving a highest F1-score of $\text{95.12 \%}$ and an AUC value of $\text{85.76 \%}$.