## 1 Introduction  
In the fourth industrial revolution, the e-commerce platform has become the most extensive system for financial institutions. People tend to select services of e-commerce and the Internet to enhance productivity and mitigate time-consuming tasks. Online transactions are becoming increasingly popular. Therefore, fraudulent activities have been increasing significantly in various industries worldwide, particularly in the financial industry. In financial companies, credit card fraud (CCF) is considered as the most problematic form of fraud and there is a need to develop tools to prevent it as soon as possible. Figure 1 indicates a summary of the total number of CCF and cents lost per 100 dollars from 2013 to 2027 worldwide [1]. The total number of CCF has increased significantly, from approximately $13.7 billion in 2013 to about $38.5 billion in 2027 globally. In the year 2021, the total CCF are predicted to lose $32.4 billion, amounting to 7.1 cents per $100 of the total volume. In order to dramatically reduce the consequences of CCF, fraud detection approaches need to be investigated in order to strictly handle it. Typically, the detection systems of CCF are trained through previous transactions to determine upcoming ones [2]. In CCF identification, the number of fraudulent cases is significantly less than under normal circumstances. This leads to the status of the imbalanced dataset. In this CCF imbalanced dataset, one class of dataset has an extremely high number of instances, while the other class accounts for only a very small number of instances. However, machine learning (ML) algorithms work effectively on the balanced distribution of classes. In order to tackle the issue of imbalanced datasets, various remedies have been researched in the past decades. In the research, three groups, known as the data-level, algorithm-level, and ensemble solutions, are commonly proposed [3]. Additionally, the deep reinforcement learning (DRL) approach has recently emerged as an effective approach for classifying imbalances. The classification problem is formulated as a sequential decision-making process and is handled via a deep Q-learning network. A classification action on one sample is performed by an agent at each time step. The classification action is evaluated through the environment and the agent receives a reward. The minority class sample receives a large reward, so the agent is more susceptible to this class. The agent eventually searches for an optimal classification policy in the imbalanced dataset based on a specific reward function and a beneficial learning environment. This approach is applied directly to several datasets, such as the modified national institute of standards and technology (MNIST) database, and the internet movie database (IMDB), and its effective performance has been demonstrated [4]. In [5], two resampling techniques, named the synthetic minority oversampling technique (SMOTE) and adaptive synthetic (ADASYN), were used to obtain the balanced CCF dataset. The machine learning (ML) algorithms, named random forest (RF), k nearest neighbors (KNN), decision tree (DT), and logistic regression (LR), were then utilized to train the balanced CCF dataset obtained from SMOTE and ADASYN. The comparison of the performance of these ML algorithms, based on these resampling techniques, was used to indicate the best case for detecting CCF. However, this study did not provide diverse algorithms to handle imbalanced CCF datasets. This research is an extension of our previous work [5]. In this research, we expand the DRL approach and include three more effective ML classifiers, such as adaptive boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), and deep neural network (DNN) in order to have diverse solutions to comprehensively develop the CCF detection systems.