# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 5 Results and Discussion  
To evaluate our proposed solution, we conducted extensive experiments using a variety of machine learning algorithms and deep learning models. The machine learning algorithms included Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), XGBoost (XGB), LightGBM (LGBM), K-Nearest Neighbors (NN), Naive Bayes (NB), AdaBoost (AB), and Bagging Classifier (BC). For deep learning, we utilized Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks, and Recurrent Neural Networks (RNNs). Although LSTM and RNN models are typically used for sequential data with temporal dependencies, we employed these models to capture complex patterns and non-linear interactions within the features. Fraud detection often involves intricate relationships between variables that may not be fully captured by conventional models. By leveraging LSTM and RNN architectures, we aimed to enhance the model's ability to identify subtle patterns indicative of fraudulent behavior, even in the absence of explicit temporal information. Additionally, our main objective is to offer a performance analysis comparing traditional machine learning models with deep learning models in detecting fraudulent transactions. These experiments aimed to assess the performance and effectiveness of our solution across various techniques and models. Figures 7-14 show the results obtained using different resampling techniques. From these figures, we observe that the outcomes are promising, demonstrating the efficiency of our proposed methods. Figure 6: The proposed solution workflow. Figure 8: Model performance metrics with AE-GAN. Figure 7: Model performance metrics with GAN. Figure 10: Model performance metrics with VAE. Figure 9: Model performance metrics with AE. Figure 11: Model performance metrics with SMOTE. Figure 12: Model performance metrics with ADASYN. Table 4 presents the performance metrics of various machine learning models using Generative Adversarial Networks for data augmentation. Among the evaluated models, XGB achieved the highest sensitivity (0.830882) and demonstrated strong performance across other metrics, including precision (0.941667), F-measure (0.882813), and G-mean (0.911490). RF also performed competitively with an F-measure of 0.877470 and a G-mean of 0.903393, indicating a balanced trade-off between sensitivity and specificity. LR and DT showed moderate sensitivity (0.669118 and 0.823529, respectively), with DT having a higher G-mean (0.907209) compared to LR (0.817924). Notably, the NB model performed poorly, with a sensitivity of 0.00000 and corresponding F-measure and G-mean values of 0.000000, suggesting its ineffectiveness in the given context. Additionally, advanced neural network models such as LSTM and ANN demonstrated robust performance, with LSTM matching the performance of BC in all metrics. Overall, tree-based ensemble methods (RF,  
Figure 14: BFDS comparison for each model across different oversampling techniques. Figure 13: BFDS scores comparison across oversampling techniques. XGB, and LGBM) consistently outperformed other models, reflecting their ability to capture complex data patterns effectively. Figure 7 provides a clear visualization of the performance metrics of various models using GAN to address class imbalance. From these plots, it is evident that XGB and RF outperform other models, achieving the highest sensitivity, precision, F-measure, and G-mean. LGBM and GB exhibit strong performance, though it is slightly lower than XGB and RF. DT performs well in sensitivity but lags in precision, which affects its overall F-measure. Simpler models like LR and KNN show moderate results, with AB performing similarly. NB and RNN struggle to adapt to GAN-generated data, performing poorly across all metrics. Table 5 displays the performance metrics of various machine learning models using Autoencoder-Generative Adversarial Networks (AE-GANs).