# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 5 Results and Discussion
This figure underscores the effectiveness of various oversampling techniques, with SMOTE and ADASYN standing out for their broad applicability and GAN and AE-GAN providing targeted improvements in specific metrics. The results in Table 17 demonstrate the performance of various oversampling techniques across different models, evaluated using BFDS. The findings show that AE-GAN consistently outperforms or remains competitive with traditional methods like SMOTE and ADSYN, particularly for complex models. For LR, AE-GAN achieves the highest BFDS \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Sensitivity** & **Precision** & **F-Measure** & **G-Mean** \\ \hline LR & 0.956 (ADASYN) & 0.864 (AE-GAN) & 0.752 (GAN) & 0.950 (SMOTE) \\ DT & 0.824 (GAN) & 0.695 (AE-GAN) & 0.747 (GAN) & 0.907 (GAN) \\ RF & 0.868 (SMOTE) & 0.949 (GAN) & 0.877 (GAN) & 0.931 (SMOTE) \\ GB & 0.956 (ADASYN) & 0.915 (AE-GAN) & 0.850 (AE-GAN) & 0.962 (ADASYN) \\ XGB & 0.890 (ADASYN) & 0.974 (AE) & 0.888 (AE) & 0.943 (ADASYN) \\ LGBM & 0.904 (ADASYN) & 0.948 (AE) & 0.869 (AE) & 0.950 (ADASYN) \\ KNN & 0.897 (SMOTE) & 0.896 (GAN) & 0.821 (GAN) & 0.946 (SMOTE) \\ NB & 0.912 (ADASYN) & 0.055 (SMOTE) & 0.104 (SMOTE) & 0.936 (ADASYN) \\ AB & 0.934 (SMOTE) & 0.798 (GAN) & 0.777 (GAN) & 0.954 (SMOTE) \\ BC & 0.809 (SMOTE) & 0.914 (AE-GAN) & 0.843 (GAN) & 0.899 (SMOTE) \\ ANN & 0.875 (SMOTE) & 0.940 (AE) & 0.832 (VAE) & 0.935 (SMOTE) \\ LSTM & 0.882 (ADASYN) & 0.907 (GAN) & 0.843 (GAN) & 0.939 (ADASYN) \\ RNN & 0.904 (SMOTE) & 0.128 (AE-GAN) & 0.181 (AE-GAN) & 0.939 (SMOTE) \\ \hline \end{tabular}
\end{table} 
Table 16: Best metrics for each model. Figure 16: Best metric scores for each model with corresponding methods. (0.453), closely followed by AE (0.452) and GAN (0.451). This indicates that generative approaches are more effective than conventional methods in addressing class imbalance for linear models. In DT and GB, ADSYN achieves the best performance (0.586 and 0.674, respectively), suggesting that simpler oversampling techniques can still be effective for these models. However, for more advanced models like RF and XGB, AE-GAN leads with BFDS values of 0.697 and 0.691, respectively, highlighting its ability to generate high-quality synthetic data that enhance fraud detection. For AB and BC, GAN achieves the highest BFDS (0.620 and 0.678, respectively), while AE-GAN remains competitive, reinforcing the strength of generative models in ensemble learning. In ANN, LSTM, and RNN, GAN achieves the best results for ANN (0.694), while AE-GAN consistently ranks in the top three for all neural models. This suggests that the hybrid AE-GAN model effectively improves class balance while maintaining strong performance across diverse architectures. For NB, SMOTE achieves the best BFDS (0.091), while generative models (AE-GAN and GAN) perform similarly (0.070). This indicates that simple generative methods may not be optimal for probabilistic models. Overall, AE-GAN ranks first or closely behind the best-performing method across 11 out of 13 models, demonstrating its ability to handle class imbalance effectively. Future work will focus on optimizing AE-GAN using Bayesian optimization and distributed metaheuristic algorithms to further enhance performance and scalability.