## 1 Introduction
### Fraud Detection System Challenges and Limitations  
Although many credit card fraud detection (CCFD) systems and frameworks have been proposed in the academic and industrial sectors, they face numerous challenges and limitations that disturb their efficiency and effectiveness. These issues demand heightened attention for resolution, encompassing concerns such as imbalanced data, adversarial attacks, feature engineering, real-time detection, the cost of false positives, and data privacy, as described below [5; 6; 7; 8; 9; 10; 11; 12; 13]:  
* Unavailability of public datasets: The unavailability of public datasets, particularly real-world credit card datasets, is attributed to confidentiality concerns, making it challenging to access data due to privacy considerations for cardholders.
* Imbalanced data and skewed class distribution: The imbalance in class distribution is a critical issue that affects prediction accuracy.
* Changes of fraud methods and patterns: Fraudsters persistently devise novel attack and fraud tactics to outwit CCFD systems, allowing them to evade the detection of both new and previously unseen fraudulent transactions. They employ various adversarial techniques such as data poisoning, evasion attacks, and manipulation of input data to deceive the model.
* Changes in cardholder's behavior: The CCFD system encounters challenges in fraud detection due to the continuous changes in cardholders' behavior over time. These changes are not uniform across all cardholders' lives.
* Concept drift: This occurs when the ML classifier model, trained on historical data, becomes outdated or less effective when deployed in a dynamic environment due to changing data distribution. This can prevent its ability to detect all fraud patterns, as expected.
* Real-time detection system: Establishing a real-time detection system is essential for effective fraud prevention. However, it becomes increasingly challenging in environments with high transaction volumes and large datasets. Any delay in fraud detection can lead to financial losses for cardholders and financial institutions.
* False alarms: A rise in false alarms can detrimentally affect the accuracy and reliability of CCFD systems, potentially causing problems for individuals and financial institutions.
* Data privacy: Data privacy and protection are crucial aspects that demand heightened focus. However, the challenge lies in accessing publicly available data for conducting experiments, presenting a significant obstacle for CCFD system developers due to these concerns.
* Many ML techniques classify one class more accurately than the other: Each machine learning (ML) or deep learning (DL) algorithm possesses its own set of advantages and disadvantages, which allow it to detect certain patterns while potentially missing others effectively. Some algorithms may perform well in detecting one class of data but struggle with others, or they may excel in specific detection systems but falter in others.  
Therefore, there is a pressing need to develop robust CCFD systems that integrate various techniques to enhance their strength and efficiency. These techniques may include cloud computing, fog computing, edge computing, IoT, blockchain, and more. Additionally,integrating a combination of machine learning (ML) or deep learning (DL) algorithms is essential to bolster the strength of the CCFD system, leveraging the advantages of each algorithm to create a robust detection system.