## 2 Related Work  
The data imbalance presents various challenges to performing data analysis and data mining in most real-world research areas, especially online banking transactions. Credit card fraud has resulted in a huge loss for both customers and financial companies worldwide. Therefore, researchers are searching for optimized methods to detect and prevent this fraud. Recently, ML approaches have been applied to detect CCF activities. In [6], the novel approach, named extreme outlier elimination utilizing k reverse nearest neighbors (kRNNs) with hybrid sampling technique, was proposed to generate reliable anticipations to not only fraud but also non-fraud circumstances. This novel approach proved its festiveness compared with other classifiers, such as C4.5, Naive Bayes (NB), KNN, and Radial Basis Function (RBF) networks. In [7], several ensemble classifiers were analyzed comprehensively through regression and voting for CCF detection. These classifiers were then compared with effective single classifiers, such as KNN, NB, Support Vector Machine (SVM), DT, RBF, and Multilayer Perceptron (MLP). These algorithms were evaluated based on three different datasets that were treated using SMOTE. In [2], the SMOTE-edited nearest neighbor (ENN) method was found to be best for detecting the CCF compared with other different classifiers among a set of oversampling approaches, and the SMOTE-Tomek's Links (TL) showed good outcomes according to the set of under-sampling techniques. In [8], the combined probabilistic and neuro-adaptive method was proposed to an identified database of credit card transactions. This combination illustrated a high classification of fraud. In [9], a hidden Markov model (HMM) was employed to create a model of the operations sequences in credit card transaction processing and showed how it was used for the identification of frauds. Through experiment results, the system based on HMM demonstrated its effectiveness and usefulness of learning expenditure profile of the card-holders. The accuracy of this system was about 80% over a wide variation in the input data. The system was also scalable to cope with massive volumes of transactions. In [10], the ML methods were used to detect anomalies in Fintech applications. The suspicious activity in the financial dataset was targeted and the models were created to predict future frauds. The available techniques of anomaly detection were also debated and showed good performance in the CCF detection. The study [11] proposed a novel hybrid approach with Dynamic Weighted Entropy (DWE) to solve the problem of class imbalance with the overlap in CCF detection, relying on a divide-and-conquer idea. In this study, a new measurement, named DWE, was developed to assist the hyper-parameters selection of the model. With the help of DWE, the model efficiency was improved, and the time consumption on finding good hyper-parameters was exceptionally decreased. Compared with other state-of-the-art methods, this method was proved to be effective through various experiments. In [12], based on the enhanced R-value, the strategy was developed to select features that ensure that the data achieve minimal overlap, thus improving the performance of the classifier for CCF detection. Three feature selection algorithms, which are Reduce Overlapping with No-sampling (RONS), Reduce Overlapping with SMOTE (ROS), and Reduce Overlapping with ADASYN (ROA), were used to mitigate the overlap degree. The outcomes indicated that the RONS algorithm was successful in detecting the vital features in model selection, whereas both ROS and ROA achieved significantly high performance in terms of the F-measure and G-mean. The effective feature selection strategy was extended as an alternative for tackling problems of class-imbalanced learning involving overlapping. In summary, there has been a variety of ways to create models for identifying CCF activities to reduce risks in the financial sector.