# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 4 Emerging Fraud Detection Techniques
### Reinforcement Learning in CCFD
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Title** & **Topic** & **Comment** \\ \hline \hline \multirow{8}{*}{([PERSON] et al., 2018) [73]} & \multirow{8}{*}{\begin{tabular}{c} Detecting Fraud in \\ Adversarial Environments: \\ A Reinforcement Learning \\ Approach \\ \end{tabular} } & \multirow{8}{*}{\begin{tabular}{c} Finance-Banking-Credit \\ Card Fraud \\ \end{tabular} } & \multirow{8}{*}{\
\begin{tabular}{c} In the study, it was stated that credit card \\ fraud is a costly problem for banks. In \\ addition, it was stated that this abuse \\ creates a great concern for consumers. \\ The disadvantages of supervised learning \\ methods used are mentioned in credit \\ card fraud. It has been stated that \\ supervised learning leads to a static \\ approach and in this case, it creates \\ weakness in recognizing the changing \\ conditions. In addition, the reinforcement \\ learning approach is more effective than \\ static approaches in modelling. In the \\ study, a hybrid structure was developed \\ by adding the Markov decision process \\ (MDP) to the reinforcement \\ learning approach. \\ \end{tabular} } \\ \hline \hline \multirow{8}{*}{([PERSON] et al., 2021) [72]} & \multirow{8}{*}{
\begin{tabular}{c} The study focused on the imbalanced \\ data set problem and its solution. In the \\ study, the raw data set and the data sets \\ obtained because of resampling (SMOTE \\ and ADASYN) were used. Machine \\ learning algorithms and the deep \\ reinforcement learning approach were \\ applied to these data sets. Models \\ obtained with different approaches (raw \\ data-machine learning, resampled data \\ sets-machine learning and raw data-deep \\ reinforcement learning) were compared \\ in terms of performance. The deep \\ reinforcement learning model using the \\ raw data set did not meet the \\ expectations in terms of performance. \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Studies on reinforcement learning in the financial world. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Title** & **Topic** & **Comment** \\ \hline \hline \multirow{8}{*}{([PERSON] and [PERSON], 2023) [75]} & & & In the study, it was stated that several machine learning algorithms can detect whether the digital transaction is fraudulent, but these algorithms fail to reduce the processing time. Hybrid models are being developed to detect movements quickly and efficiently. A new approach (RDQN) was obtained by combining the rough set theory approach and the deep reinforcement learning approach. The study consists of three main titles: data preprocessing process, determination of structural relations with rough set theory, and establishment of hybrid system (DQN) by determining properties. In particular, with the improvement in the reward function, the processing time is considerably reduced. \\ \hline \multirow{8}{*}{([PERSON] et al., 2021) [74]} & & In the study, it was mentioned that the models developed with standard machine learning algorithms could not give optimum results in the desired solutions. A decision model was created by adding the benefit maximization to the reward function in the reinforcement learning approach. The developed model was compared with other standard classifiers. The developed model gave very successful results in terms of performance compared to other classification models except for XGBoost. \\ \hline \multirow{8}{*}{([PERSON] et al., 2021) [76]} & & & In the study, credit prices were created on an individual basis by using individual customer information such as credit scores, maturity, and number of applications. While creating the credit scorecard used in pricing, the past information of individual customers was used. It was attempted to create a model pattern with the deep reinforcement learning approach. \\ \hline \multirow{8}{*}{([PERSON], 2021) [77]} & & & The study tested whether social biases affect credit scorecards. Reinforcement learning approach was used in the analysis of factors and evaluation of results in this test process.