# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 5 Results and Discussion
Among neural network models, ANN achieved the highest sensitivity (0.838235) and F-measure (0.832117), showing its strength in handling the complex data generated by VAE. In contrast, the NB model performed the worst, with a sensitivity of 0.022059 and an F-measure of 0.000466, making it ineffective for this dataset. Logistic Regression (LR) also struggled, with a low F-measure of 0.039565 despite a relatively high G-mean (0.913741). While DT and BC delivered moderate performance, boosting-based models like GB and AB underperformed in terms of sensitivity (0.669118 and 0.551471, respectively). Overall, ensemble methods--particularly RF, XGB, and LGBM--consistently achieved superior performance, while NB and simpler models like LR were less effective in learning from VAE-augmented data. Figure 10 shows the performance of various models using Variational Autoencoder (VAE) for fraud detection. XGB and RF excel with high accuracy, specificity, and balanced sensitivity, precision, and F-measure. LGBM also performs well, achieving high specificity and good sensitivity (80.88%) with a strong G-mean. LR and DT show good specificity but struggle with precision and F-measure, limiting their fraud detection ability. NB underperforms with low sensitivity and precision. The LSTM model demonstrates a good balance, with a high G-mean (87.02%), while the RNN model has low sensitivity and precision. XGB and RF are the top performers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Sensitivity** & **Precision** & **F-Measure** & **G-Mean** \\ \hline LR & 0.930679 & 0.930733 & 0.897059 & 0.020229 & 0.039565 & 0.913741 \\ DT & 0.997179 & 0.997515 & 0.786765 & 0.335423 & 0.470330 & 0.885895 \\ RF & 0.999555 & 0.999871 & 0.801471 & 0.908333 & 0.851562 & 0.895191 \\ GB & 0.998151 & 0.998675 & 0.669118 & 0.446078 & 0.535294 & 0.817454 \\ XGB & 0.999532 & 0.999848 & 0.801471 & 0.893443 & 0.844961 & 0.895181 \\ LGBM & 0.999216 & 0.999519 & 0.808824 & 0.728477 & 0.766551 & 0.899130 \\ KNN & 0.999462 & 0.999859 & 0.750000 & 0.894737 & 0.816000 & 0.865964 \\ NB & 0.849256 & 0.850575 & 0.022059 & 0.000235 & 0.000466 & 0.136977 \\ AB & 0.992791 & 0.993494 & 0.551471 & 0.119048 & 0.195822 & 0.740191 \\ BC & 0.998830 & 0.999179 & 0.779412 & 0.602273 & 0.679487 & 0.882481 \\ ANN & 0.999462 & 0.999719 & 0.838235 & 0.826087 & 0.832117 & 0.915423 \\ LSTM & 0.999438 & 0.999824 & 0.757353 & 0.872881 & 0.811024 & 0.870184 \\ RNN & 0.995658 & 0.996788 & 0.286765 & 0.124601 & 0.173719 & 0.534643 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model performance metrics with VAE. Table 8 presents the performance metrics of various machine learning models using the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Among the models, Random Forest (RF) achieved the best overall performance, with a sensitivity of 0.867647, a precision of 0.855072, and an F-measure of 0.861314, highlighting its strong ability to detect minority class samples while maintaining high accuracy. XGB and ANN models also performed competitively, with XGB achieving a sensitivity of 0.860294 and an F-measure of 0.790541, while ANN recorded a sensitivity of 0.875000 and an F-measure of 0.777778, showcasing their robustness in learning from the oversampled data. LGBM achieved a similar sensitivity (0.867647) but had a lower precision (0.504274), resulting in a lower F-measure (0.637838), indicating a trade-off between detecting positive samples and minimizing false positives. In contrast, simpler models like LR and NB struggled with low precision (0.052764 and 0.055274, respectively) and F-measures (0.099842 and 0.104031, respectively), despite having relatively high sensitivity (0.926471 for LR and 0.882353 for NB), reflecting their difficulty in handling the increased complexity of the SMOTE data. DT and BC displayed moderate performance, with sensitivities of 0.750000 and 0.808824, respectively, and F-measures of 0.481132 and 0.698413.