# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 4 Emerging Fraud Detection Techniques
### Proposed Conceptual Model  
In this study, RL approaches are recommended instead of SL approaches and a conceptual model is proposed utilizing deep RL, deep Q learning, and a deep Q network for more effective CCFD, as depicted in Figure 2. As illustrated, it is proposed to develop an end-to-end analytical structure for card fraud. The proposed model includes many processes, from adding and editing data to databases to evaluating the real-time activity of the end user. In the first stage, credit card transactions, customer information, and external knowledge are managed in a data mart. The information of different data marts are brought together on a transaction/customer basis to create samples for training, testing, validation, and scoring. When creating these samples, data sets should be created that can reflect the population. Also, in this process, samples should not differ from each other at a high level. The new training data set is balanced by applying IL (oversampling, undersampling, and hybrid approaches) techniques to the training data set. During the balancing process, transactions are made on rows in the training data set. Important features are determined on a column basis by applying different FS (filter, wrapper, and combined techniques) methods on training fraud data set balanced by resampling. After these two pre-processing stages are completed, the agent-based training process is started with learning agents. Specifically, learning agents are run on training data sets, which are organized both on  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Title** & **Topic** & **Comment** \\ \hline \multirow{4}{*}{([PERSON] et al., 2022) [80]} & Applications of & \multirow{4}{*}{ \begin{tabular}{c} The study focused on improving the buying and selling of individual financial assets in the investment decision process. In the study, the agent is trained in buying/selling transactions, and its behavior is tested on new data. \\ \end{tabular}  } \\  & Reinforcement Learning in & & \multirow{4}{*}{ \begin{tabular}{c} Finance-Banking- \\ Investment Management \\ \end{tabular}  } \\  & Conbule Deep Q-Network & & & \\  & & & \\  & & & \\ \hline \multirow{4}{*}{([PERSON] et al., 2021) [81]} & FinRL-Podracer: High & \multirow{4}{*}{ \begin{tabular}{c} The study offered a deep reinforcement learning approach cloud solution with \\ high performance and high scalability in \\ investment decisions. The deep \\ reinforcement learning approach has a \\ constantly renewing mechanism to \\ improve investment performance. \\ \end{tabular}  } \\  & Conbule Deep Reinforcement & \multirow{4}{*}{ \begin{tabular}{c} Finance-Banking- \\ Investment Management \\ \end{tabular}  } \\  & Conbule Deep Reinforcement & & & \\  & Learning for Quantitative & & & \\  & Finance & & & \\  & Finance & & & \\ \hline \multirow{4}{*}{([PERSON] and [PERSON], 2019) [79]} & \multirow{4}{*}{ \begin{tabular}{c} Deep Reinforcement \\ Learning for Optimizing \\ Finance Portfolio \\ Management \\ \end{tabular}  } \\  & Conbule Deep Reinforcement & \multirow{4}{*}{\ \begin{tabular}{c} Finance-Banking- \\ Portfolio Management \\ \end{tabular}  } \\  & Conbule Learning for Optimizing & \multirow{4}{*}{ \begin{tabular}{c} Finance-Banking- \\ portfolio Management \\ \end{tabular}  } \\  
row and column basis, to analyze card transactions. Learning agents maximize the total rewards by determining the patterns of abnormal behavior on the fraud training data set using deep learning. In this paper, equation-based and agent-based model trials were conducted. Model trials with SVM, RF, and double deep Q network (DDQN) approaches were conducted to support the proposed conceptual model. The European Credit Cardholder (2013) raw data set was used in model trials. On the raw data set, SMOTEENN was applied as the IL approach and ANOVA F test was applied as the FS approach. It is seen that the DDQN model, which is an ABM approach, is more successful than benchmarking models (SVM and RF).