# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 5 Conclusions and Future Works  
Due to increasing card fraud transactions, banks are faced with cash and reputational losses. In order to minimize fraud-related losses in banks, detection models are developed. These fraud models should evaluate abnormal transactions immediately. In implementation with real time, CCFD models are expected to be fast, effective, and consistent. CCFD models should not only be high-performance models but also contribute to the prevention process. In this paper, many distinguished papers on the modelling of credit card fraud transactions have been examined. The methods used in CCFD studies have been classified and evaluated as existing techniques and emerging techniques. The approaches of existing techniques, which are frequently used in CCFD studies, have been examined in detail and the results have been interpreted. Furthermore, the benefits of modern emerging techniques to CCFD studies are also revealed in this paper. On the card fraud data set, it is thought that the use of hybrid approaches, which are a combination of oversampling and undersampling approaches, will increase in the process of dealing with imbalanced data. In imbalanced data sets, RL-based models give very successful results compared to other classification models. It is also thought that the DDQN approach, which is one of the RL approaches, will be effective on the card fraud data set. It is very important that the developed model patterns are usable in real-time and do not cause Type I or Type II errors. In particular, during the examination of model performance, the emergence and ungovernableness of Type I and Type II errors can cause important problems in card fraud management. To solve possible problems in Type I and Type II, the Q value can be divided into many groups and examined with binning methods such as bucket and quantile. The binning process can be divided into groups by considering the Q value and the actual fraud flag. Actions for the prevention process can be determined by making risk-free, low-risk, and risky assignments to groups developed according to the level of risk. Model deployment and online scoring studies are among the most important issues in the credit card fraud detection and prevention process. The system deployment process of models is a very difficult issue for the credit card fraud topic. Model deployment is among the issues awaiting a solution regarding credit card fraud. Apart from the model deployment topic, online scoring studies on credit card fraud should also be carried out on a transaction basis. \begin{table}
\begin{tabular}{c c c c} \hline
**Risk Group** & **Binning** & **Legal Rate** & **Fraud Rate** \\ \hline
1 & Q Value \textless{} 0.2831 & 99.97\% & 0.03\% \\
2 & 0.2831 \textless{} Q Value \textless{} 0.2899 & 99.94\% & 0.06\% \\
3 & 0. 2899 \textless{} Q Value \textless{} 0. 2945 & 99.90\% & 0.10\% \\
4 & 0. 2945 \textless{} Q Value \textless{} 0. 2985 & 99.88\% & 0.12\% \\
5 & 0. 2985 \textless{} Q Value \textless{} 0.3031 & 99.64\% & 0.36\% \\
6 & 0.3031 \textless{} Q Value & 99.52\% & 0.48\% \\ \hline \end{tabular}
\end{table} 
Table 7: The implementing of example binning approach for risk groups. In the world of credit card fraud, online scoring is one of the issues awaiting solutions to take quick action. This paper has many potential benefits for the card fraud process. This paper offers suggestions in many different areas, from the data governance process to the action determination processes. It is suggested that the card data set in the big data category can be managed via cloud platforms. In the model development process, it is suggested that ABM approaches can be used effectively as well as EBM approaches. Integrating CCFD studies with prevention processes will benefit card fraud management processes. There are some limitations in this paper. The performance of the developed models could have been examined on different fraud data sets. However, the reluctance of banks to share data limits this study.