Credit Card Fraud: Analysis of Feature Extraction Techniques for Ensemble Hidden Markov Model Prediction Approach  
[PERSON]  
1 Department of Computer Science, Tai Solarin University of Education, Jiggun, Jiebu-Ode 2118, Nigeria; [EMAIL_ADDRESS] (A.O. ); [EMAIL_ADDRESS] (O.O.) [PERSON]  
2 French-South African Institute of Technology, Department of Electrical, Electronic, and Computer Engineering, Cape Peninsula University of Technology, Bellville Campus, Bellville 7535, South Africa; [EMAIL_ADDRESS] (O.B. ); [EMAIL_ADDRESS] (V.B.) [PERSON]  
1 Department of Computer Science, Tai Solarin University of Education, Jiggun, Jiebu-Ode 2118, Nigeria; [EMAIL_ADDRESS] (A.O. ); [EMAIL_ADDRESS] (O.O.) [PERSON]  
1 Department of Computer Science, Tai Solarin University of Education, Jiggun, Jiebu-Ode 2118, Nigeria; [EMAIL_ADDRESS] (A.O. ); [EMAIL_ADDRESS] (O.O.) Footnote 17 of the time, the victims of fraud are oblivious that the perpetrators have access to their credit card information, especially when these credit cards are used for payment on phishing websites [2; 3]. The quickest way to spot these credit card frauds is to examine each card's spending patterns and look for any differences from regular spending patterns. However, it is complex to achieve because the daily number of credit card transactions is huge, resulting in large transactional information. As a result, there has been a lot of recent study into the exact, rapid, and efficient prediction of credit card fraud. In recent times, machine learning (ML) tools have been deployed in the literature to efficiently predict credit card frauds. ML tools enable computers to improve their forecasting abilities by learning from previous datasets. Different ML tools, such as hidden Markov models (HMMs), decision trees, K-nearest neighbour, logistic regression, and so on, have been deployed for the prediction of credit card frauds [4; 5; 6; 7; 8]. However, work is being carried out to improve the predictive ability of these ML tools. HMM is a popular and flexible ML tool that can easily model randomly changing datasets. HMMs can easily predict fraudulent transactions from a sequence of defined observations. Nonetheless, the ability of HMM to effectively predict these credit card frauds depend on the adopted feature extraction technique. This implies that the more reliable the output of the feature extraction technique, the better the prediction performance of HMM [9; 10; 11]. Likewise, the length of the outputted feature vector, which doubles as the dimension of HMM, determines the computational time complexity imposed on HMM. The longer the length of the feature vector, the more the computational time complexity imposed on HMM [12]. Therefore, it is paramount to carefully select the feature attraction technique that will be combined with HMM to balance the trade-off between its performance gain and the computational time complexity. Accordingly, this article analyses two feature extraction techniques that can be combined with HMM for the prediction of credit card fraud. First, the principal component analysis (PCA) [13; 14] technique is combined with HMM for the prediction of credit card fraud. In addition to employing PCA, this article computationally determines the \"optimal\" feature vector length that will balance the trade-off between the PCA performance gain and the computational time complexity it imposed on HMM. It was discovered that this 'optimal' feature vector length is not computational time-efficient when PCA is combined with HMM. Therefore, the features derived using PCA are converted to statistical features to reduce the computational time complexity imposed on HMM. The proposed robust but simple statistical features denoted as MRE (Mean, Relative Amplitude, and Entropy) are merged to form a feature vector that can be combined with HMM to effectively predict credit card frauds. Furthermore, as highlighted in [12], the Gaussian emission distribution parameters of HMM are sensitive to a flat start or random values, which impedes prediction performance.