# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Techniques** & **GAN** & **AE-GAN** & **AE** & **VAE** & **SMOTE** & **ADASYN** \\ \hline
**GAN** & - & 0.6939 & 0.7542 & 0.0024 & 0.1271 & 0.0061 \\
**AE-GAN** & - & - & 0.0572 & 0.0002 & 0.0339 & 0.0104 \\
**AE** & - & - & - & 0.0075 & 0.0839 & 0.0134 \\
**VAE** & - & - & - & - & 0.0409 & 0.6848 \\
**SMOTE** & - & - & - & - & - & 0.0803 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Wilcoxon test \(p\)-values for BFDS comparison. This line illustrates AE-GAN's superior performance in improving the balance between sensitivity and precision compared to other techniques. In contrast, lines for other oversampling methods, such as SMOTE and ADASYN, generally display lower BFDS scores, with the lines often positioned beneath the orange AE-GAN line. This indicates that while these techniques are effective, they do not achieve the same level of enhancement in model performance. Techniques like GAN, AE, and VAE show even lower BFDS scores, as their lines remain further below the AE-GAN line, reflecting their reduced effectiveness. The lines for NB and RNN also trail at the lower end, highlighting their difficulties in achieving balanced performance. Overall, the orange AE-GAN line underscores its role as the most effective oversampling technique for maximizing BFDS scores, surpassing other methods in enhancing model performance. Table 16 shows the obtained performance metrics for each model, highlighting the best sensitivity, precision, F-measure, and G-mean values along with the corresponding oversampling techniques. The analysis reveals that different oversampling techniques have a notable impact on model performance. For instance, the AB model achieves the highest sensitivity (0.933824) and G-mean (0.953585) using the SMOTE technique, indicating a strong capability in detecting positive instances and maintaining a balanced performance. In contrast, the ANN model exhibits superior precision (0.940476) with the AE technique, demonstrating its effectiveness in reducing false positives, and performs well in F-measure (0.832117) with VAE. The BC model, utilizing AE-GAN, excels in precision (0.913793), showcasing its proficiency in correctly classifying positive instances. The DT model achieves the highest G-mean (0.907209) with GAN, reflecting balanced performance but with lower sensitivity and precision. Techniques such as SMOTE and ADASYN generally improve sensitivity and G-mean across several models, highlighting their efficacy in managing class imbalance. Conversely, AE and VAE techniques improve precision, as demonstrated by ANN and XGB. Notably, the NB model shows a significant trade-off with high sensitivity (0.911765) but low precision (0.055274) using SMOTE. These results emphasize the importance of selecting appropriate oversampling techniques to balance the trade-offs between sensitivity, precision, and overall model performance. Figure 15: Oversampling techniques comparison across models (BFDS). Figure 16 presents the best metric scores in terms of sensitivity, precision, F-measure, and G-mean across various models, along with the associated oversampling techniques. SMOTE is the most frequently appearing method, demonstrating its broad effectiveness, particularly excelling in G-mean and sensitivity for models such as RF, KNN, and AB. GAN also shows significant utility, notably enhancing precision and F-measure in models like DT and RF, highlighting its strength in balancing sensitivity and precision. ADASYN is employed in several instances, achieving impressive results in sensitivity and G-mean for models including LR, GB, and LSTM. AE-GAN appears less frequently but is notable for improving F-measure in models like GB and LSTM. AE and VAE are the least appearing techniques, with AE showing strong performance in F-measure for XGB and VAE excelling in precision for DT.