Recently, ML algorithms have been widely applied for credit card fraud detection [23,24,25]. Researchers have used both traditional ML and deep learning (DL) algorithms to predict credit card fraud efficiently. For example, Alarfaj et al. [26] conducted a study using ML and DL techniques for detecting credit card fraud, while Van Belle et al. [27] employed inductive graph representation learning, Esenogho et al. [28] used a neural network ensemble, and Zhang et al. [29] employed an ensemble classifier based on isolation forest and adaptive boosting.

Some problems encountered when dealing with credit card datasets include high dimensionality and imbalance class [30,31], making it difficult for ML classifiers to learn and make accurate predictions. In addition, high dimensional data often make the learning process complex and computationally expensive, resulting in models with poor generalization ability [32]. Therefore, feature selection is essential in such datasets to reduce the computational burden and enhance the model’s generalization ability. For example, Chaquet-Ulldemolins et al. [33] recorded an increase in the classification performance of ML classifiers after introducing feature selection. Generally, feature-selection methods are useful in applications where the number of features affects the classifier’s performance.

The wrapper feature-selection methods have been widely applied in numerous applications [34,35]. They compute the importance of each feature based on its usefulness when training the ML model. The primary components of a wrapper method are the learning classifier and search strategy. The wrapper technique exists as a wrapper around the learning classifier and uses the same classifier to select the most relevant features. Therefore, a robust learning classifier could enhance the wrapper-based feature selection. Furthermore, the search strategy employed in the wrapper could affect the feature selection, and using the right search strategy for a given application is crucial in obtaining good performance.

Evolutionary search techniques such as genetic algorithms can avoid becoming stuck in local optima. Unlike deterministic algorithms, they can identify reduced feature sets that can effectively represent the original feature set [36]. The GA-based wrapper can easily identify feature redundancy and correlations. In addition, selecting a suitable classifier is vital in developing robust GA wrapper models since the wrapper procedure is tied to the selected classifier’s performance. However, there are specific issues to consider when selecting the classifier. Firstly, the classifier should be able to achieve good classification performance and have excellent generalization ability. Secondly, since the classifier would be used to train numerous subsets, it should have good training speed. Thirdly, the number of features in the various subsets might differ. Therefore, using the same model parameters might not be enough to obtain good performance in all the subsets [37]. Hence, it would be preferred to use a classifier that automatically updates the model parameters for every feature subset to achieve good performance.

Other recent methods for credit card fraud detection include a signal processing framework [38], signal processing on graphs [39], and a deep learning ensemble [40]. In addition, in the literature, several learning algorithms (such as decision tree [41], naïve Bayes [42], SVM [43], and random forest [44]) have been used as the classifier in the GA wrapper. However, these classifiers are not able to consider the issues mentioned above. Therefore, a hybrid wrapper approach that considers all the above-mentioned issues is proposed. The proposed approach employs the IG-based filter feature selection to rank the attributes, and only the top-ranked features would be used as input into the GA wrapper. Meanwhile, the GA wrapper employs the ELM as the learning classifier. The ELM can achieve excellent classification performance and generalization ability with an extremely fast learning speed compared to conventional training methods. Furthermore, unlike traditional neural networks based on backpropagation algorithms, the ELM’s training process is entirely automatic and does not require it to be tuned iteratively.