# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 5 Results and Discussion
Among the models, XGB achieved the best overall performance, with the highest sensitivity (0.808824), precision (0.964912), F-measure (0.880000), and a G-mean of 0.899325, indicating its superior ability to balance false positives and false negatives. LGBM also performed well, with a sensitivity of 0.816176 and an F-measure of 0.850575, reflecting its effectiveness in handling complex patterns. RF and GB achieved similar performance levels, with F-measure values of 0.844622 and 0.850394, respectively, demonstrating the robustness of ensemble-based methods. In contrast, NB again performed poorly, with sensitivity, precision, and F-measure values of 0.000000, indicating its inability to capture meaningful patterns in the AE-GAN-enhanced dataset. Neural network models such as LSTM and ANN showed moderate performance, with LSTM achieving a higher sensitivity (0.801471) and F-measure (0.822642) compared to ANN. The RNN model exhibited the weakest performance among deep learning methods, with a low sensitivity (0.308824) and an F-measure of 0.181425. Overall, tree-based ensemble methods, particularly XGB and LGBM, consistently outperformed other models, highlighting their adaptability and effectiveness when paired with AE-GAN-generated data. Figure 8 visualizes the performance metrics of various models using AE-GAN to address class imbalance in fraud detection. XGB achieved the highest accuracy at 99.96%, with Naive Bayes (NB) showing perfect specificity but zero sensitivity, limiting its usefulness. Sensitivity was highest in XGB, LGBM, and LSTM, at around 80-81%. XGB also achieved the highest precision and F-measure, demonstrating its strong fraud detection ability. G-mean scores were highest in XGB and LGBM, reflecting their balanced performance. The \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Sensitivity** & **Precision** & **F-Measure** & **G-Mean** \\ \hline LR & 0.999298 & 0.999824 & 0.669118 & 0.858491 & 0.752066 & 0.817924 \\ DT & 0.999111 & 0.999390 & 0.823529 & 0.682927 & 0.746667 & 0.907209 \\ RF & 0.999637 & 0.999930 & 0.816176 & 0.948718 & 0.877470 & 0.903393 \\ GB & 0.999473 & 0.999777 & 0.808824 & 0.852713 & 0.830189 & 0.899246 \\ XGB & 0.999649 & 0.99918 & 0.830882 & 0.941667 & 0.882813 & 0.911490 \\ LGBM & 0.999509 & 0.999871 & 0.823529 & 0.910569 & 0.864865 & 0.907427 \\ KNN & 0.999473 & 0.999859 & 0.8757353 & 0.895652 & 0.820717 & 0.870199 \\ NB & 0.998408 & 1.000000 & 0.000000 & 0.000000 & 0.000000 & 0.000000 \\ AB & 0.999309 & 0.999695 & 0.757353 & 0.798450 & 0.777358 & 0.870128 \\ BC & 0.999532 & 0.999871 & 0.786765 & 0.906780 & 0.842520 & 0.886940 \\ ANN & 0.999380 & 0.999812 & 0.727941 & 0.860870 & 0.788845 & 0.853115 \\ LSTM & 0.999532 & 0.999871 & 0.786765 & 0.906780 & 0.842520 & 0.886940 \\ RNN & 0.993551 & 0.995053 & 0.051471 & 0.016317 & 0.024779 & 0.226309 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance metrics using GAN. RNN model showed lower performance across all metrics, particularly in sensitivity and precision, indicating its limited effectiveness. Table 6 presents the performance metrics of various machine learning models using Autoencoder (AE) for feature extraction. XGB achieved the best overall performance, with the highest sensitivity (0.816176), precision (0.973684), and F-measure (0.888000), along with a G-mean of 0.903409, indicating its effectiveness in maintaining a balance between sensitivity and specificity. LGBM also performed competitively, with a sensitivity of 0.801471 and an F-measure of 0.868526, demonstrating its robustness in handling the AE-transformed data. RF followed closely with a sensitivity of 0.786765 and an F-measure of 0.856000, further confirming the strength of ensemble-based approaches. Conversely, the NB model again performed poorly, yielding sensitivity, precision, and F-measure values of 0.000000, making it unsuitable for this dataset.