# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 2 Theoretical Background
### Credit Card Fraud Detection  
As implied above, technological progress and digitalization have brought many fraud concepts, particularly credit card fraud in banking causing negative situations such as cash damage and loss of trust, into consideration [16]. According to the Federal Trade Commission report, card fraud attacks were recorded as 277.739 in 2019, 399.721 in 2020, 395.391 in 2021, 448.459 in 2022, and 425.977 in 2023. The report estimates that 2024 will also be an increasing year. In relation to credit cards, fraudsters use many different channels for fraud attempts. Credit card fraud types are depicted in Figure 1. Card fraud attempts that are carried out specifically for physical cards such as counterfeits, lost, stolen, or non-receipt have been used for a long time. In addition, application and account takeover fraud methods are frequently used by fraudsters in digital environments.  
Although there are some attempts to create a solution to this problem, it is still very difficult to analyze, model, detect, and prevent credit card-related fraud situations. In [17], it is reported that the fraud-related credit card transactions do not make up even 1% of all credit card transactions, and therefore model patterns are difficult to distinguish as compared to regular balanced transactions. In order to prevent credit card misuse, semi-manual decision systems consisting of rules based on the expert opinion are to be employed. However, it is known that there are some patterns that cannot be noticed with rules created by experience and intuition. More intelligent systems capable of detecting misuse of credit cards through utilization of other artificial intelligence methodologies are necessary [18].  
When generating intelligent systems, it should be taken into account that the credit card fraud studies are based on two important structures. The first structure is the process of managing the data that will be used in fraud studies. In this context, card-level transactions are combined with customer information and credit bureau information to develop a fraud data mart. In the second structure, enough samples are taken from the fraud data mart within the appropriate time interval to create training, testing, and validation of the data sets. This process is run on three successive operations as given below:  
* Imbalanced Learning (balancing the imbalanced card fraud data set);
* Feature Selection (detecting the important variables on the card fraud data set);
* Predictive Model (development of card fraud models for possible prediction).  
At the end of several runs (trials), the card fraud detection models developed are evaluated in terms of a set of performance metrics. By benchmarking the trials, the most appropriate one is determined as the \"champion fraud detection model\". In developing the champion model, SL or semi-supervised learning (SSL) are used.  
Fraud flag information must be fed completely, fully, and accurately into all card fraud data sets, especially in data marts for the sake of better learning. Once defining and running the champion model, the output of the model can be fed into a prevention model defining respective action plans against fraudulent data. Note that the existing credit card fraud modelling approaches as well as emerging credit card fraud modelling approaches are reviewed in this paper. In addition, the existing modelling approaches such as classification are mainly based on equation-based traditional techniques whereas the emerging models are agent-based techniques, as proposed in this paper. ABM techniques are also elaborated below for validating the proposed model.  
Figure 1: Credit card fraud attacks.