## 2 Related Work  
The widespread use of credit cards for online purchases has also increased the possibility of fraud. According to the Nigerian Deposit Insurance Corporation (NDIC) annual report of 2018, between 2016 and 2018, the number of credit card fraud incidents in Nigeria increased by 33%, while the actual amount lost to this credit card fraud climbed by 84% [19]. Likewise, the federal trade commission (FTC) affirmed that there were around 1579 data breaches, totalling 179 million data points, with credit card fraud being the most widespread [6; 20]. Therefore, a wide range of ML and data mining techniques have been deployed in the literature to proffer solutions to this menace. This section reviews some recent and related credit card prediction techniques. A weighted average ensemble algorithm for credit card fraud detection was proposed in [21]. This algorithm integrated multiple models, including logistic regression (LR), K-nearest neighbour (KNN), random forest (RF), AdaBoost, and Bagging, to enhance detection accuracy. The study utilized a dataset from a European credit card company and achieved an accuracy of 99%, surpassing the baseline models (LR, AdaBoost, RF, KNN, and Bagging) in performance. The authors demonstrated the potential of utilizing an ensemble model for real-time credit card fraud detection. However, the lack of details regarding the feature extraction process limits the reproducibility of their work. In [22], a comparative study on four ML techniques, namely, DT, RF, LR, and Naive Bayes (NB), for the detection of credit card frauds was carried out, with attention on imbalanced datasets. Their analysis revealed that RF was effective at detecting credit card fraud, while LR and NB performed comparably, and DT had the worst performance. However, similar to [21], the study did not provide insight into the feature extraction process, raising concerns about the reproducibility of their results. The authors in [23] conducted an extensive review of various machine learning techniques used for detecting credit card fraud. The review encompassed methods such as NB, support vector machines (SVMs), multilayer feed forward (MLFF) neural networks, Bayesian belief networks (BBNs), genetic algorithms (GAs), and classification and regression trees (CARTs). As this article was a critical analysis of prior studies, no specific datasetwas employed. The findings highlighted the superior performance of NB, which achieved the highest accuracy, followed by SVM and GA. In [24], the sequence of operation of credit card transactions was modelled using HMMs.Also, the paper provided information on how HMMs can be used to detect fraudulent credit card transactions. Yet, the paper did not document any clear results to buttress the performance of their developed HMM. More importantly, the paper did not provide information on the feature extraction technique used or combined with their developed HMM. As mentioned earlier, the feature extraction technique used with any ML technique including HMMs determines the performance of the ML technique, hence, the focus of this article. The authors in [25] modelled a fraud detection system that would attempt to detect credit card fraud as accurately as possible by producing clusters and analysing the clusters formed by the dataset for anomalies. Therefore, their work examined the detection accuracy of two hybrid techniques: the K-MC technique with multilayer perceptron (MLP) and the K-MC technique with HMM. The authors show that the detection accuracy of the two models examined was fairly the same. Nonetheless, the paper was silent on the adopted feature extraction technique which is a major factor in analysing the performances of their proposed models. The process of fraud detection using HMMs was described in [26]. In their work, the researchers combined the HMM process with the K-MC technique to form what this article described as an EHMM. The K-MC technique was used to initialise the HMM process to improve its performance.