Machine learning (ML) uses algorithms with the complexity to iterate over massive datasets to analyse the data for past behaviour with the aim to predict future outcomes. Financial institutions are using ML to detect Credit Card Fraud (CCF) by learning the patterns that distinguish between legitimate and fraudulent actions from historic data of credit card transactions to combat CCF. The market economic order has been negatively affected by CCF, which has contributed to low consumer confidence in financial institutions, and loss of interest from investors. The CCF loses continue increasing every year despite existing efforts to prevent fraud, which amount to billions of dollars lost annually. ML techniques consume large volumes of historical credit card transaction data as examples for learning. In ordinary credit card datasets, there are far fewer fraudulent transactions than legitimate transactions. In dealing with the credit card data imbalance problem, the ideal solution must have low bias, low variance, and high accuracy. The aim of this study was to provide an in-depth experimental investigation of the effect of using the data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The study focused on finding a novel way to handle imbalanced data, to improve the performance of ML algorithms in identifying fraud or anomaly patterns in massive amounts of financial transaction records, where the class distribution was imbalanced. The experiment led to the introduction of two unique multi-level hybrid data-point approach solutions, namely, Feature Selection with Near Miss Undersampling; and Feature Selection with SMOTe based Oversampling. The results were obtained using four widely used ML algorithms, namely, Random Forest, Support Vector Machine, Decision Tree, and Logistic Regression to build the classifiers. These algorithms were implemented for classification of credit card datasets and the performance was assessed using selected performance metrics. The findings show that using the data-point approach improved the predictive accuracy of the ML fraud detection solution.