## 8 Conclusions and Future Works  
In this study, we comprehensively analyze the imbalanced CCF dataset through the DRL and the ML algorithms. It is due to the fact that these analyses are considered as one of the crucial factors in building the speed and effectiveness of the system detection of CCF. First, the processing data stage is applied to this CCF dataset to increase the effectiveness of these models. Second, we resample the imbalanced CCF dataset based on two simple but effective resampling techniques, i.e., SMOTE and ADSYN through two resampling approaches: resampling approach 1 and resampling approach 2. The difference between the resampling approach 1 and the resampling approach 2 is the position where SMOTE and ADASYN are used to resample the imbalanced CCF datasets. These ML algorithms are then applied to the balanced CCF datasets, which are accomplished from SMOTE and ADASYN based on the two resampling approaches. Next, the distinguishing classification evaluation indexes are utilized to prove the effectiveness of the models of CCF detection. The classification metrics consist of three primary types, known as fundamental, combined, and graphical assessments. Through our experiments, all of the results of the indexes of the classification metrics are nearly perfect in the resampling approach 1. However, in the resampling approach 2, the results have a big gap among the indexes. This is mainly because that resampling is performed for the full sample in the resampling approach 1. Based on the resampling approach 2, the precision, F1 score, and MCC show low results in LR and AdaBoost, while RF and XGBoost represent stable results in all the indexes of classification metrics. Furthermore, it is obvious that the results of the ML classifiers for CCF detection that are based on the SMOTE technique are better than the results which are based on the ADASYN algorithm. This is mainly because ADASYN has two weaknesses. The first weakness is each neighborhood can only include one minority example in terms of minority examples sparsely distributed. The second one is that the accuracy of this technique suffers owing to its adaptable nature [44]. Finally, the DRL, which is considered as the novel approach, is used for the imbalanced CCF dataset, but it does not accomplish results that are as good as we expected. In conclusion, our comprehensive empirical results with the imbalanced CCF datasets in the banking sector have shown that the performance metrics of ML algorithms based on two resampling approaches are better than that of DRL with the imbalanced dataset. RF and XGBoost are considered as the best approaches to cope with the CCF dataset based on two resampling approaches. On the other hand, TP and TN rates based on two resampling approaches show an opposite trend. The rates of TP are greater than the TN rates based on the resampling approach 1, while the rates of TP are less than the TN rates in the resampling approach 2. This leads to unexpected results for detecting CCF in several indexes of classification measurements in the resampling approach 2. In the future, we want to collect precisely and extend the dataset through financial companies to accomplish an exhaustive model. We continue to improve the credit card fraud detection system through other different ML and deep learning approaches. In addition, extending and applying ML techniques over highly imbalanced datasets to \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **F1 Score** & **G-Mean** &
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} \\ \cline{2-9}  & 34.8\% & 0.067\% & 25.17\% & 34.82\% & 13.27\% & 29.60\% & 29.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Classification metrics for DRL on imbalanced CCF dataset.