# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 3 Existing Fraud Detection Techniques
### Supervised Learning Models in CCFD
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Solutions for Imbalance Data** &
\begin{tabular}{c} **Feature Selection** \\ **Methods** \\ \end{tabular} & **Classification Methods** & **The Best Model** \\ \hline
[38] & \begin{tabular}{c} SMOTE, Borderline SMOTE, \\ ADASYN, SMOTEENN, and \\ SMOTETomek \\ \end{tabular} & PCA & \
\begin{tabular}{c} KNN, LR, LTDA, \\ CART, and NB \\ \end{tabular} &
\begin{tabular}{c} CART Model with \\ Borderline SMOTE \\ \end{tabular} \\ \hline
[39] & SMOTE & RFE & \
\begin{tabular}{c} PAC, LDA, RNC, BNB, \\ GNB, and ETC \\ \end{tabular} &
\begin{tabular}{c} ETC \\ \end{tabular} \\ \hline
[40] & \begin{tabular}{c} SMOTE, ADASYN, ROS, RUS, TMLK, \\ CCS, AIIKNN, SMOTETomek, and \\ SMOTEENN \\ \end{tabular} & PCA & \
\begin{tabular}{c} AdaBoost, XGBoost, RF, \\ SVM, LR, GNB, MNB, \\ KNN, and DT \\ \end{tabular} &
\begin{tabular}{c} AdaBoost, XGBoost, \\ and RF \\ \end{tabular} \\ \hline
[41] & \
\begin{tabular}{c} SMOTE, Borderline SMOTE, k-Means \\ SMOTE, and ADASYN \\ \end{tabular} & PCA, AE, and VAE & CNN and SVM &
\begin{tabular}{c} CNN Model with \\ ADASYN \\ \end{tabular} \\ \hline
[42] & \begin{tabular}{c} SMOTEENN \\ \end{tabular} & CRA & \
\begin{tabular}{c} LR, SVM, NB, RF, \\ LGBM, XGBoost, \\ AdaBoost, and DT \\ \end{tabular} &
\begin{tabular}{c} AdaBoost + LGBM \\ (Hybrid Model) \\ \end{tabular} \\ \hline
[43] & RUS & PCA & LR, XGBoost, and MLP & MLP \\ \hline
[44] & SMOTE & PCA &
\begin{tabular}{c} LR, LDA, NB, and \\ XGBoost \\ \end{tabular} & XGBoost \\ \hline
[45] & \begin{tabular}{c} RUS, TMLK, OSS, CDNN, ENN, \\ AllKNN, RENN, NCR, NMS, IHT, \\ ROS, ADASYN, SMOTE, SVM \\ SMOTE, Borderline SMOTE, \\ SMOTEENN, and SMOTETomek \\ \end{tabular} & PCA & \
\begin{tabular}{c} LR, DT, KNN, RF, NB, \\ GBM, CatBoost, LGBM, \\ and XGBoost \\ \end{tabular} &
\begin{tabular}{c} CatBoost Model with \\ AllKNN \\ \end{tabular} \\ \hline
[46] & RUS & PCA & DT, LR, NB, and EL & EL \\ \hline
[19] & \
\begin{tabular}{c} SMOTE, Borderline SMOTE, and \\ ADASYN \\ \end{tabular} & PCA and MRMR & DT, RF, and ETC &
\begin{tabular}{c} ETC Model with \\ Borderline SMOTE \\ \end{tabular} \\ \hline
[47] & SMOTE & PCA &
\begin{tabular}{c} SVM, ELM, and \\ XGBoost \\ \end{tabular} & XGBoost \\ \hline
[48] & \
\begin{tabular}{c} RUS, SAUS, ROS, ADASYN, and \\ SMOTE \\ \end{tabular} & PCA &
\begin{tabular}{c} MLP, GRU, LSTM, and \\ LSTM-CRF \\ \end{tabular} & LSTM-CRF \\ \hline
[49] & RUS &
\begin{tabular}{c} t-SNE, PCA, and \\ SVD \\ \end{tabular} & LR, KNN, DT, and SVM & LR \\ \hline
[28] & ROS & PCA and AE & LGBM, KNN, and RF & LGBM \\ \hline
[50] & SMOTE & PCA, AE, and \\  & OCSVM &
\begin{tabular}{c} LR, KNN, DT, RF, NB, \\ AdaBoost, and \\ XGBoost \\ \end{tabular} & XGBoost \\ \hline
[51] & RUS and ROS & PCA & LR & LR \\ \hline
[52] & SMOTEENN & Shapiro &
\begin{tabular}{c} LR, KNN, DT, RF, NB, \\ AdaBoost, and \\ XGBoost \\ \end{tabular} & RF \\ \hline
[25] & RUS & CRA &
\begin{tabular}{c} LR, GNB, KNN, DT, \\ and RF \\ \end{tabular} & RF \\ \hline
[53] & RUS, ROS, and SMOTE & PCA &
\begin{tabular}{c} LR, DT, XGBoost, and \\ MLP \\ \end{tabular} & XGBoost Model with \\ \hline
[54] & SMOTE & PCA & ETC, GBM, DT, and RF &
\begin{tabular}{c} GBM Model with \\ AdaBoost and RF \\ Model with AdaBoost \\ \end{tabular} \\ \hline
[27] & SMOTEENN & PCA &
\begin{tabular}{c} k-Means, Weighted \\ k-Means, and \\ PSO-Weighted k-Means \\ \end{tabular} & PSO-Weighted k-Means \\ \end{tabular}
\end{table} 
Table 3: _Cont._When reviewing 34 distinguished papers, it is realized that advanced level undersampling and oversampling approaches have been used in addition to the basic level random over sampling (ROS) and random under sampling (RUS) approaches. As can be seen in Table 3, the SMOTE oversampling approach has been the most used method in resampling for CCFD between 2020 and 2024. It is also shown that the use of the ADASYN oversampling approach has increased for the balancing of imbalance fraud data sets.