## 3 Results
### Performance Evaluation on Test Data  
A comparative evaluation of model performance on test data from both the original imbalanced dataset and the augmented dataset was performed. The results on precision, recall, and F1 scores are presented in Table 13.  
In identifying defaulters, LR reported a decrease in recall (from 0.90 to 0.86) after data augmentation, which subsequently decreased the F1 score (from 0.88 to 0.87). For non-defaulter classification, a significant increase in recall was reported (from 0.65 to 0.72) after data augmentation, resulting in a maintained F1 score of 0.70. The performance of the SVM model on the other hand showed inconsistencies. Despite an improvement in the precision in identifying defaulters, the F1 score declined (0.80 to 0.69) due to decreased recall from 0.79 to 0.57. However, the augmentation improved sensitivity to the minority non-defaulter class, where recall increased from 0.55 to 0.80, which improved the F1 score from 0.54 to 0.57. The ensemble classifier models reported improved F1 scores after data augmentation. The F1 score for the defaulter class improved from 0.91 to 0.92, while the F1 score of the non-defaulter class also increased significantly from 0.77 to 0.83. With respect to the GBM model, the F1 score for the defaulter class remained the same at 0.91 after augmentation, while an improvement in recall (0.76 to 0.86) for the non-defaulter class enhanced the F1 score from 0.79 to 0.81. The XGB model also maintained high predictive performance for both classes. Among defaulters, the F1 score improved from 0.91 to 0.92, while the F1 score among non-defaulters also increased from 0.79 to 0.83. The ANN model indicated the most consistent improvement driven by balanced increases in precision and recall. The F1 score increased from 0.89 to 0.92 for the defaulter class, while for non-defaulters, the F1 score increased from 0.74 to 0.81. Figure 9 illustrates the ROC curves and AUC values for the five machine learning models evaluated on unseen data. On the original dataset, the ensemble-based classifiers reported comparative performance with an AUC of 0.92 for RF, 94 for the GBM, and 94 for XGB. The ANN reported an AUC of 0.88, while the SVM reported the lowest AUC of 0.74. Following data augmentation, there were observable differences in the AUC values. RF and XGB reported the highest AUC of 0.95 each, the AUC value of the ANN model increased from 0.88 to 0.93. On the other hand, LR and the GBM maintained the same AUC values compared to their \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{**Original Data**} & \multicolumn{4}{c}{**Augmented Data**} \\ \cline{3-8}  & & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{2}{*}{**LR**} & Defaulters & 0.86 & 0.90 & 0.88 & 0.87 & 0.86 & 0.87 \\  & Non-defaulters & 0.75 & 0.65 & 0.70 & 0.69 & 0.72 & 0.70 \\ \hline \multirow{2}{*}{**SVM**} & Defaulters & 0.80 & 0.79 & 0.80 & 0.86 & 0.57 & 0.69 \\  & Non-defaulters & 0.54 & 0.55 & 0.54 & 0.45 & 0.80 & 0.57 \\ \hline \multirow{2}{*}{**RF**} & Defaulters & 0.88 & 0.94 & 0.91 & 0.94 & 0.90 & 0.92 \\  & Non-defaulters & 0.84 & 0.70 & 0.77 & 0.79 & 0.87 & 0.83 \\ \hline \multirow{2}{*}{**GBM**} & Defaulters & 0.90 & 0.93 & 0.91 & 0.93 & 0.89 & 0.91 \\  & Non-defaulters & 0.83 & 0.76 & 0.79 & 0.78 & 0.86 & 0.81 \\ \hline \multirow{2}{*}{**XGB**} & Defaulters & 0.90 & 0.91 & 0.91 & 0.93 & 0.91 & 0.92 \\  & Non-defaulters & 0.80 & 0.77 & 0.79 & 0.81 & 0.85 & 0.83 \\ \hline \multirow{2}{*}{**ANN**} & Defaulters & 0.87 & 0.92 & 0.89 & 0.91 & 0.93 & 0.92 \\  & Non-defaulters & 0.79 & 0.69 & 0.74 & 0.83 & 0.79 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance results of machine learning models on unseen data. performance on the original data. Although the SVM's AUC increased from 0.74 to 0.77, the model still reported the lowest AUC.