# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 1 Introduction  
With technological progress and the digital transformation of business in the financial world, the use of credit cards is significantly increasing. Due to their fast operation and ease of use, credit cards are frequently preferred by customers as a means of payment [1]. Although huge amounts of card transaction data are collected and kept in the databases of the banks, not much attention is given to automatically interpreting these data. It is important to organize, manage, and apply a suitable pre-processing process and generate analysis methodologies for the sake of better interpretation. By applying appropriate analytical methods, it is possible to discover specific data patterns and provide this information to respective units or systems for the sake of risk management, marketing, and credit allocation. It is now obvious that the banks may frequently face some risks, especially depending on card utilization [2]. Illegal actions, such as fraud, can create severe financial problems and destroy the reputation of financial enterprises. Note that some fraud models have already been developed in order to detect and prevent undesired situations [3]. Itis proven that it is possible to prevent cash and reputational losses caused by fraudulent transactions. However, there are some issues such as balancing the data set, identifying the qualified features, etc., that still need to be sorted. A review of the literature as well as an explanation of the general training process is provided below. Note that when dealing with fraud transactions using credit cards, the data sets generated are mainly imbalanced due to target variability [4]. This necessitates some pre-processing methods such as resampling to be employed before use and then imbalanced learning (IL) and training methods should be employed to balance the data. The qualified features of data sets are also presented as input to the classification algorithms forming the basis of supervised learning (SL) in order to identify the data patterns. The literature provides several models used in training as well as a comparison with respect to performance metrics such as accuracy, recall, precision, f1-score, lift curve, etc. [5]. This comparison naturally highlights the so-called \"champion model\" in respective training performance as it can be used to identify whether the transactions are fraudulent or not. Similarly, some hybrid versions have been created by combining both oversampling and undersampling techniques in order to regulate the imbalanced data set [6]. With hybrid approaches such as oversampling followed by undersampling, the raw data set is converted in order to resample and use in generating the training model. The reason for using the hybrid approach is that the oversampling followed by undersampling approaches contribute to the model development process by removing noise on the raw data set [7]. In addition, overfitting problems resulting from oversampling and loss of information through undersampling can be solved with hybrid models. Note that these approaches are mainly used in several anomaly detection studies, including fraudulent transactions. The literature indicates some SL algorithms that are used to identify fraud transactions. Generally, equation-based modelling (EBM) is applied for this purpose. The dynamic nature of the data creates difficulty in learning that takes the attention of the research community to implement agent-based modelling (ABM), as stated in [8]. Due to the nature of intelligent agents, it is now obvious that the alerts and interactions of autonomous agents can provide the required flexibility and dynamism in credit card data processing [9]. It is also well known that the agents can respond to the changes in the data and generate/recommend immediate actions. This may well increase the performance of the fraud detection systems. In this paper, that is the main reason for proposing an ABM-based detection system instead of pure SL algorithms.