# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 4 Emerging Fraud Detection Techniques
### Reinforcement Learning in CCFD  
Reinforcement learning (RL) is an ABM technique in which the learning process is carried out by being influenced by the actions of an agent and its environment [67; 68]. In RL, agents evaluate information in the environment (situation), and they affect the environment (action) with their obtained information [69; 70]. Unlike SL, RL uses positive and negative behaviors as rewards and punishments by providing feedback between input and output [71]. Therefore, RL is very successful in detecting, evaluating, and using the behavior of information in data sets. The RL approach is frequently used in several financial areas such as card fraud, credit allocation, credit pricing, credit scoring, and investment and portfolio management. As stated in [72; 73], RL has recently been used instead of SL, especially in fraud studies carried out via POS or digital transactions. In addition, the model performance of the RL models has been found to be very high compared to the supervised learning models [74]. RL models attract attention not only with their performance and impact in financial matters but also with their speed in processing and response. In terms of processing time, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Title** & **Topic** & **Comment** \\ \hline \multirow{6}{*}{([PERSON] and [PERSON], 2020) [60]} & \multirow{6}{*}{Macroeconomic Impacts of Trade Credit—an Agent Modelling Exploration} & \multirow{6}{*}{Finance–Banking-Credit Transactions} & In the study, the effects of macroeconomic changes on commercial loans were examined by evaluating the macroeconomic effects in various dimensions. Two different types of agent-based model were developed in the study (down-stream firms and up-stream firms). The results show that there is a potential relationship between the ratio of non-performing loans and the average product of firms. \\ \cline{1-1}  & & & In the study, an agent model was used to examine how reward system design affects bank credit losses. The model results show that a collaborative reward system has the potential to decrease bank credit losses. \\ \hline \multirow{6}{*}{([PERSON], 2015) [59]} & \multirow{6}{*}{The Effects of Reward System on Bank Credit Losses—an Agent Model} & \multirow{6}{*}{Finance–Banking-Credit Risk} & In the study, an agent model was used to examine how reward system design affects bank credit losses. The model results show that a collaborative reward system has the potential to decrease bank credit losses. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._financial fraud models with RL are considerably lower than financial fraud models with supervised learning [75]. In credit allocation and credit pricing studies, RL models have been found to positively affect processes in terms of scaling, computational efficiency and fast training [76]. As mentioned in [77, 78], in credit scoring, RL has also been used in subjects such as testing the model effects and determining the optimal threshold value. Cutoff strategies, which are an important issue in the credit scoring, can also be easily carried out with RL approach studies. In addition to credit-based studies, RL approaches are also used in studies such as investment and portfolio management for the buying and selling of financial assets. RL has frequently begun to be used in the fields of investment and portfolio management from decision processes to optimal policies [79, 80]. It is thought that the RL approach will make a great contribution to several different financial issues [81]. It is anticipated that financial modelling studies using the RL approach will increase. Table 5 depicts the studies on RL applications in the financial world.