# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 5 Results and Discussion
Neural network models displayed mixed results, with LSTM achieving better performance (F-measure of 0.809160) than ANN (0.718182), while the RNN model exhibited the weakest performance across all metrics (sensitivity of 0.029412 and F-measure of 0.000765), indicating challenges in learning from AE-transformed data. DT and Bagging Classifier (BC) showed moderate performance, with G-means of 0.878411 and 0.870199, respectively. Overall, tree-based ensemble models, especially XGB and LGBM, outperformed other models, highlighting their superior ability to extract meaningful patterns from AE-enhanced datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Sensitivity** & **Precision** & **F-Measure** & **G-Mean** \\ \hline LR & 0.999181 & 0.999801 & 0.610294 & 0.830000 & 0.703390 & 0.781135 \\ DT & 0.999052 & 0.999414 & 0.772059 & 0.677419 & 0.721649 & 0.878411 \\ RF & 0.999579 & 0.999918 & 0.786765 & 0.938596 & 0.856000 & 0.886961 \\ GB & 0.999427 & 0.999789 & 0.772059 & 0.853659 & 0.810811 & 0.878576 \\ XGB & 0.999672 & 0.999965 & 0.816176 & 0.973684 & 0.888000 & 0.903409 \\ LGBM & 0.999614 & 0.999930 & 0.801471 & 0.947826 & 0.868526 & 0.895217 \\ KNN & 0.999462 & 0.999859 & 0.750000 & 0.894737 & 0.816000 & 0.865964 \\ NB & 0.998408 & 1.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\ AB & 0.998982 & 0.999613 & 0.602941 & 0.713043 & 0.653386 & 0.776343 \\ BC & 0.999473 & 0.999859 & 0.757353 & 0.895652 & 0.820717 & 0.870199 \\ ANN & 0.999274 & 0.999941 & 0.580882 & 0.940476 & 0.718182 & 0.762134 \\ LSTM & 0.999415 & 0.999766 & 0.779412 & 0.841270 & 0.809160 & 0.882740 \\ RNN & 0.87778 & 0.879131 & 0.029412 & 0.000388 & 0.000765 & 0.160800 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model performance metrics using AE. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Sensitivity** & **Precision** & **F-Measure** & **G-Mean** \\ \hline LR & 0.999286 & 0.999836 & 0.654412 & 0.864078 & 0.744770 & 0.808891 \\ DT & 0.999111 & 0.999449 & 0.786765 & 0.694805 & 0.737931 & 0.886753 \\ RF & 0.999544 & 0.999894 & 0.779412 & 0.921739 & 0.844622 & 0.882796 \\ GB & 0.999555 & 0.999883 & 0.794118 & 0.915254 & 0.850394 & 0.891081 \\ XGB & 0.999649 & 0.999953 & 0.808824 & 0.964912 & 0.880000 & 0.899325 \\ LGBM & 0.999544 & 0.999836 & 0.816176 & 0.888000 & 0.850575 & 0.903351 \\ KNN & 0.999462 & 0.999859 & 0.750000 & 0.894737 & 0.816000 & 0.865964 \\ NB & 0.998408 & 1.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\ AB & 0.999111 & 0.999695 & 0.632353 & 0.767857 & 0.693548 & 0.795085 \\ BC & 0.999532 Figure 9 visualizes the performance metrics of various models using AE to address class imbalance in fraud detection. XGB achieved the highest accuracy at 99.97%, with NB showing perfect specificity but zero sensitivity, limiting its usefulness. Sensitivity was highest in XGB and LGBM, with values of 81.6% and 80.1%, respectively. XGB also achieved the highest precision (97.37%) and F-measure, reflecting its strong fraud detection ability. G-mean scores were highest in XGB and LGBM, indicating their robustness in fraud detection. The RNN model performed poorly across all metrics, particularly in sensitivity, precision, and G-mean, highlighting its limited effectiveness in fraud detection. Table 7 shows the performance metrics of various machine learning models using Variational Autoencoder (VAE) for data augmentation. RF and XGB demonstrated the best overall performance, both achieving a sensitivity of 0.801471 and comparable F-measure values of 0.851562 and 0.844961, respectively. RF slightly outperformed XGB in terms of precision (0.908333 vs. 0.893443), suggesting it is more effective at minimizing false positives. LGBM also performed well, with a sensitivity of 0.808824 and a G-mean of 0.899130, indicating a balanced ability to detect positive instances while maintaining high specificity.